Updated on 2024/03/30

写真a

 
YADA,Katsutoshi
 
Organization
Faculty of Business and Commerce Professor
Title
Professor
Contact information
メールアドレス
External link

Degree

  • Doctor of Business Administration ( 2002.12 )

  • Master of Business Administration ( 1994.3 )

Research Areas

  • Humanities & Social Sciences / Business administration

  • Informatics / Intelligent informatics

  • Humanities & Social Sciences / Library and information science, humanistic and social informatics

Education

  • Kobe University of Commerce   Graduate School, Division of Administration

    1997

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    Country: Japan

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Professional Memberships

Committee Memberships

  • 人工知能学会   理事  

    2020.7 - 2022.7   

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    Committee type:Academic society

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  • 経営情報学会   代議員  

    2022.4 - Present   

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    Committee type:Academic society

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  • 人工知能学会   代議員  

    2015 - Present   

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    Committee type:Academic society

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Papers

  • Sequential classification of customer behavior based on sequence-to-sequence learning with gated-attention neural networks Reviewed

    Licheng Zhao, Yi Zuo, Katsutoshi Yada

    Advances in Data Analysis and Classification   2022.8

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    Authorship:Last author   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1007/s11634-022-00517-3

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    Other Link: https://link.springer.com/article/10.1007/s11634-022-00517-3/fulltext.html

  • Customer Behavior Analysis and Classification Based on Process Mining.

    Meijun Liu, Licheng Zhao, Fengmei Sun, Weizheng Zhao, Yi Zuo, Katsutoshi Yada

    2021 IEEE International Conference on Systems, Man, and Cybernetics(SMC)   1000 - 1005   2021

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/SMC52423.2021.9659063

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    Other Link: https://dblp.uni-trier.de/db/conf/smc/smc2021.html#LiuZSZZY21

  • Application of Long Short-term Memory Based Neural Network for Classification of Customer Behavior.

    Licheng Zhao, Yi Zuo, Katsutoshi Yada, Meijun Liu

    2021 IEEE International Conference on Systems, Man, and Cybernetics(SMC)   994 - 999   2021

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/SMC52423.2021.9658703

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    Other Link: https://dblp.uni-trier.de/db/conf/smc/smc2021.html#ZhaoZYL21

  • Short-term Impact of Item-based Loyalty Program on Customer Purchase Behaviors Reviewed

    Bo Wu, Yi Sun, Katsutoshi Yada

    The Review of Socionetwork Strategies   2020.8

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    Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1007/s12626-020-00062-5

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    Other Link: http://link.springer.com/article/10.1007/s12626-020-00062-5/fulltext.html

  • Duration of Price Promotion and Retail Profit: An In-depth Study Based on Point-of-Sale Data Reviewed

    李 振, 善如 悠介, 矢田 勝俊

    Journal of Retailing and Consumer Services   accepted   2020

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  • How Shoppers Walk and Shop in a Supermarket.

    Katsutoshi Yada, Ken Ishibashi, Taku Ohashi, Danhua Wang, Shusaku Tsumoto

    114 - 118   2020

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    Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1109/ICDMW51313.2020.00025

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    Other Link: https://dblp.uni-trier.de/db/conf/icdm/icdm2020w.html#YadaIOWT20

  • Study of the Effects of Visual Complexity and Consumer Experience on Visual Attention and Purchase Behavior through the Use of Eye Tracking Reviewed

    Ken Ishibashi, Chen Xiao, Katsutoshi Yada

    Proceedings of 2019 IEEE International Conference on Big Data (Big Data)   2664 - 2673   2019.12

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  • Analysis of Customer Segmentation Based on Broad Learning System

    Zhenyu Wang, Yi Zuo, Tieshan Li, C. L. Philip Chen, Katsutoshi Yada

    2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)   2019.12

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/spac49953.2019.237870

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  • Assessment of Effect of POP on Purchase Behavior: Comparison of Effectiveness of Eye-racking Data and Shopping Path Data Reviewed

    Ken Ishibashi, Katsutoshi Yada

    Proceedings of 2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)   70 - 76   2019.10

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  • The impact of self-control on search behavior Reviewed

    Gu, H, YADA,Katsutoshi

    Procedia Computer Science, Elsevier   Volume 159   2137 - 2143   2019.9

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

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  • Analysis of social influence of shopping path by using ecological system of ants Reviewed

    Ken Ishibashi, Katsutoshi Yada

    Procedia Computer Science   159   2162 - 2171   2019.9

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  • Analysis of social influence on in-storepurchase behavior by using ecological system of ants Reviewed

    Ken, Ishibashi, YADA,Katsutoshi

    Procedia Computer Science, Elsevier   Volume 159   2162 - 2172   2019.9

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  • The Short-Term Impact of an Item-Based Loyalty Program Reviewed

    K. Yada, Y. Sun, B. Wu

    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)   pp1842-1847   2018.10

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  • A framework of ASP for shopping path analysis Reviewed

    K. Yada, K. Ichikawa, K. Takai, K. Miayazaki

    Proc. of the 4th Asia-Pacific World Congress on Computer Science and Engineering 2017   pp.49-54   49 - 54   2018.10

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    In this paper, we propose an ASP system for shopping path analysis and a cloud based system with the necessary analysis service for marketing strategies that combines sales data with shopping path data. This paper explains a recommendation system based on position information or an application that calculates a basic marketing indicator using shopping path data by introducing a framework of ASP for shopping path analysis. Because the proposed ASP service is built on a cloud based system, the users can easily access the service through the web based interface and perform large-scale data processing using the computational resources of the cloud based system at low cost.

    DOI: 10.1109/APWConCSE.2017.00017

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  • An Empirical Study of the Relationship Among Self-Control, Price Promotions and Consumer Purchase Behavior Reviewed

    X. Zhong, K. Ishibashi, K. Yada

    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)   pp1863-1868   2018.10

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  • Application of Network Analysis Techniques for Customer In-store Behavior in Supermarket Reviewed

    Y. Zuo, K. Yada, T. Li, P. Chen

    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)   pp1857-1862   2018.10

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/smc.2018.00322

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  • 視線追跡データを用いた消費者の店舗内購買行動の分析 Reviewed

    Y. Kaneko, K. Ishibashi, K. Yada

    経営情報学会 PACIS2018全国研究発表大会要旨集   pp103-106   2018.8

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  • 店舗内の時系列な行動が購買行為に与える効果に関する研究 Reviewed

    K. Ishibashi, K. Miayazaki, K. Yada

    オペレーションズ・リサーチ   Vol 62 pp789-794 ( 12 )   789 - 794   2017.12

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    Language:Japanese   Publisher:日本オペレーションズ・リサーチ学会 ; 1956-  

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  • The Effect of Crowding on Visit Ratio at an Product Area: Based on RFID Data in a Japanese Supermarket Reviewed

    B. Wu, K.Yada

    Proc. APWConCSE2017 (4th Asia-Pacific World Congress on Computer Science and Engineering)   2017.12

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    Language:English   Publishing type:Research paper (international conference proceedings)  

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  • スケールの階層性から探るスーパーマーケットの消費者行動

    Y. Kaneko, K. Yada

    オペレーションズ・リサーチ   Vol 62 pp807-814 ( 12 )   807 - 814   2017.12

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    Language:Japanese   Publisher:日本オペレーションズ・リサーチ学会 ; 1956-  

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  • ベイジアンネットワークを用いた消費者行動モデルの構築実験 Reviewed

    Y. Zuo, K. Yada

    オペレーションズ・リサーチ   Vol 62 pp795-800 ( 12 )   795 - 800   2017.12

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    Language:Japanese   Publisher:日本オペレーションズ・リサーチ学会 ; 1956-  

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  • A Framework of Recommendation System Based on In-store Behavior Reviewed

    Wai Tik So, K. Yada

    Proceedings of the 4th Multidisciplinary International Social Networks Conference,   pp1-4   2017.7

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  • Prediction of Consumer Purchasing in a Grocery Store Using Machine Learning Techniques Reviewed

    Yi Zuo, Katsutoshi Yada, A.B.M. Shawkat Ali

    Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016   18 - 25   2017.6

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Institute of Electrical and Electronics Engineers Inc.  

    Over the past decades, prediction of costumers' purchase behavior has been significantly considered, and completely recognized as one of the most significant research topics in consumer behavior researches. While we attempt to measure response of purchase intention to the contextual factors such as customers' age, gender and income, product price and sale promotion, most of business models are basing on a linear equation to estimate weight of these factors due to the linear equation is not only intuitive for other academics to compare and replicate but also luminous to explain the results for business practitioners. Nevertheless, comparing with other research fields (e.g. pattern recognition and text classification), the prediction methods for purchase behavior are overconcentration of the linear models, especially linear discriminant analysis and logistic regression analysis. On the other hand, as more and more information and communication technologies (ICT, e.g. POS and sensor) are introduced into retail, marketing and management to collect business data, the volumes of data are increasing in exponential growth. Analysis based on linear models are insufficient to satisfy the requirement of academics and practitioners any more, and machine learning techniques have been increasingly attracted us to conduct them as an alternative approach for knowledge discovery and data mining. With regard to these issues, this paper employs two representative machine learning methods: Bayes classifier and support vector machine (SVM) and investigates the performance of them with the data in the real world.

    DOI: 10.1109/APWC-on-CSE.2016.015

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  • The Influence of Customer Movement between Sales Areas on Sales Amount: A Dynamic Bayesian Model of the In-store Customer Movement and Sales Relationship

    Yuta Kaneko, Shinya Miyazaki, Katsutoshi Yada

    Procedia Computer Science   112   1845 - 1854   2017

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Elsevier B.V.  

    Recent years have seen active research that utilizes information combining geographic data and sensor data, called geospatial information, in urban planning, medical care and marketing. In this study, we focus on RFID technology that records position information (i.e., spatial information) of shopping carts in a supermarket, and estimate the latent space-time structure of the store as observation data of customers' visits. Then, we propose a dynamic Bayesian model for sales analysis, which extends the conventional state-space model to include the spatiotemporal structure. From the results of the model analysis, it is obvious that supermarkets have clear periodic structures in units of time periods and weekly structures, and they are dynamically related to the adjacency of each sales area. By utilizing the visualization of the space-time structure of the sales area, it is possible to easily inform the store manager about the influence of customers' visits on sales outcomes.

    DOI: 10.1016/j.procs.2017.08.225

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  • Model selection for financial statement analysis: Comparison of models developed by using data mining technique Reviewed

    K. Ishibashi, T. Iwasaki, S. Otomasa, K. Yada

    Proceedings of IEEE International Conference on System, Man, and Cybernetics   pp81-86   2017

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  • Do Sales Promotions Affect Dynamic Changes in Sales Outcomes: Estimation of Dynamic State of Product Sales Reviewed

    K. Yada, Y. Kaneko

    In Proceedings of the 4th Asia Pacific World Congress on Computer Science and Engineering 2017   pp1-8   2017

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  • Does the Existence of Private-Label Brands Really Impede National Brands Sales? Empirical Evidence Based on POS Data Reviewed

    Z. Li, K. Yada

    Proceedings of 2016,3rd International Conference of Asian Marketing Associations   pp. 1-17   2016.10

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  • Complementary Relationship between Private Brands and National Brands: Empirical Evidence Based on POS Data Reviewed

    Z. Li, K. Yada

    Proceedings of 2016 ,38th ISMS Marketing Science Conference,   pp. 31-43   2016.6

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  • Shop area visit ratio, stay time and sales outcomes in depth analysis based on RFID data Reviewed

    Z. Li, K. Ishibashi, K. Takai, K. Yada

    Proc. of the 3rd Asia-Pacific World Congress on Computer Science and Engineering 2016   pp.1-7   2016.1

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  • Model selection for financial statement analysis: Variable selection with data mining technique

    Ken Ishibashi, Takuya Iwasaki, Shota Otomasa, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS: PROCEEDINGS OF THE 20TH INTERNATIONAL CONFERENCE KES-2016   96   1681 - 1690   2016

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    The purpose of this study is to verify the effectiveness of a data-driven approach for financial statement analysis. In the area of accounting, variable selection for construction of models to predict firm's earnings based on financial statement data has been addressed from perspectives of corporate valuation theory, etc., but there has not been enough verification based on data mining techniques. In this paper, an attempt was made to verify the applicability of variable selection for the construction of an earnings prediction model by using recent data mining techniques. From analysis results, a method that considers the interaction among variables and the redundancy of model could be effective for financial statement data. (C) 2016 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2016.08.216

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  • Vehicle Ownership and Economic Development Reviewed

    Z. Li, K. Ishibashi, Y. Kaneko, K. Miayazaki, H. Shioji, K. Yada

    Proc. of 3rd Asia-Pacific World Congress on Computer Science and Engineering   pp171-180   2016

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  • A Deep Learning Approach for the Prediction of Retail Store Sales

    Yuta Kaneko, Katsutoshi Yada

    2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)   pp531-537   531 - 537   2016

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    The purpose of this research is to construct a sales prediction model for retail stores using the deep learning approach, which has gained significant attention in the rapidly developing field of machine learning in recent years. Using such a model for analysis, an approach to store management could be formulated. The present study uses three years' worth of point-of-sale (POS) data from a retail store to construct a sales prediction model that, given the sales of a particular day, predicts the changes in sales on the following day. As a result, a deep learning model that considers the L1 regularization achieved a sale forecasting accuracy rate of 86%. The products at the retail store have been finely categorized. Even if the attributes of the product categories are increased in number from tens to thousands, the predictive accuracy did not fall by more than about 7%. In contrast, the accuracy decreased by around 13% when the logistic regression model was used. These results indicate that deep learning is highly suitable for constructing models that include multi-attribute variables. The present research demonstrates that deep learning is effective for analyzing the POS data of retail stores.

    DOI: 10.1109/ICDMW.2016.154

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  • Clustering of customer shopping paths in Japanese grocery stores

    Natsuki Sano, Reo Tsutsui, Katsutoshi Yada, Tomomichi Suzuki

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS: PROCEEDINGS OF THE 20TH INTERNATIONAL CONFERENCE KES-2016   96   1314 - 1322   2016

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    Data about the shopping paths of customers in stores are now available due to developments in radio frequency identification technology. In this study, we conducted clustering of the shopping paths of customers gathered in a grocery store in Japan. We obtained nine typical movement patterns from the clustering results. In addition, we associated the customers' shopping paths with purchase results obtained from point of sales data. We examined the movement patterns in terms of their demographic features, purchase results, stay time ratio for each sales area, and the shapes of the movement patterns. (C) 2016 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2016.08.176

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  • Fractal Dimension of Shopping Path: Influence on Purchase Behavior in a Supermarket

    Yuta Kaneko, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS: PROCEEDINGS OF THE 20TH INTERNATIONAL CONFERENCE KES-2016   96   1764 - 1771   2016

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    This paper aims to use fractal dimensions to quantify the complexity of customer in-store movements, and proposes a purchase model factoring in the effects of complex customer movements on purchase behavior. We used the box-counting method to calculate the fractal dimension of shopping paths and investigated its relationships with basket size and sales, which are viewed as important for marketing. We found that the customer group with high fractal dimensions had mean values for the number of sales floor zones visited, basket size, stay time in store, and sales amount statistically higher than those of the customer group with lower fractal dimensions. We analyzed a binomial logit model to identify positive effects that the fractal dimension has on purchases in the food ingredient categories of vegetable, fish, and meat. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

    DOI: 10.1016/j.procs.2016.08.225

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  • The influence of sales areas and bargain sales on customer behavior in a grocery store

    Natsuki Sano, Katsutoshi Yada

    NEURAL COMPUTING & APPLICATIONS   26 ( 2 )   355 - 361   2015.2

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:SPRINGER  

    Developments in radio frequency identification (RFID) technology have resulted in the availability of data on customers' movement paths in various stores. In this paper, we propose a customer behavior model in a grocery store by using RFID and point-of-sales data. This model is based on a nonhomogeneous hidden Markov model with covariates and estimates "Stop" and "Pass by" behaviors. The model introduces sales areas and the number of bargain products as covariates and quantifies the effect of these covariates on each behavior. Thus, we can diagnose sales areas and decide the optimal quantity of bargain products. Further, we can rearrange sales areas and reinforce weak sales areas according to the diagnosis results. In addition, information on the optimal quantity of bargain products allows implementation of an effective bargain sales strategy.

    DOI: 10.1007/s00521-014-1619-8

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  • Using Statistical Learning Theory for Purchase Behavior Prediction via Direct Observation of In-store Behavior Reviewed

    Y. Zuo, K. Yada

    Proc. of the 2nd Asia-Pacific World Congress on Computer Science and Engineering 2015   pp.1-6   2015.1

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  • Recommendation system for grocery store considering data sparsity

    Natsuki Sano, Natsumi Machino, Katsutoshi Yada, Tomomichi Suzuki

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015   60   1406 - 1413   2015

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    In grocery stores, large-scale transaction data with identification, such as point of sales (POS) data, is being accumulated as a result of the introduction of frequent shopper programs. We propose two recommendation systems based on transaction data of a grocery store. In recommending product items in grocery stores, data sparsity is a problem. This is because individual customers only purchase very few of the total number of product items a store sells. We evaluate various recommendation methods including SVD-type recommendation based on real POS data and summarize methods suitable for the proposed recommendation systems. (C) 2015 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2015.08.216

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  • Visualization System for Shopping Path

    Yuta Kaneko, Shinya Miyazaki, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015   60   1772 - 1779   2015

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    The purpose of this research paper is to render the customer-shopping path and customer existence probability visible such that people in the marketing field can easily grasp customer behaviors in store. To achieve this, we introduced the customer existence probability, which provides a visual of how long customers stay in each sales floor zone. The visualization system was then applied to customer-shopping path data to provide separate and concurrent displays of customer-shopping paths and the customer existence probability. Concurrent display of customer-shopping paths/customer existence probability is suitable for visualization of longer customer stays in-store, where the customer moves a great deal. This display enables easy assessment of in-store customer rounds including length of time. It also incorporates to the maximum the information volume of customer-shopping path data. (C) 2015 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2015.08.287

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  • Verification of effect on next purchase when many vice category products are brought

    Ken Ishibashi, Kei Miyazaki, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015   60   1780 - 1787   2015

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    The purpose of this article is to verify the effect of previous purchases on later purchases, using shopping path data. We focus on the effect of vice category products bought before. In existing research, the effect of the prior purchase of virtue category products (which are relative necessities) on later purchases, is explained as licensing effect. Based on earlier studies, the purchase of vice category products is also expected to impact later purchase behavior. For example, prior vice category products purchased may impel customers to purchase virtue category products. But the verification result clearly indicated that prior purchase of vice category products did not impel customers to buy virtue category products, rather it indirectly led to a tendency to refrain from further purchases of products. (C) 2015 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2015.08.288

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  • A Bayesian network approach for predicting purchase behavior via direct observation of in-store behavior Reviewed

    Yi Zuo, Katsutoshi Yada, Eisuke Kita

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   9505   61 - 75   2015

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Springer Verlag  

    In strategic management of retail industry, the advanced investigation by using radio frequency identification (RFID) technology to capture customers’ in-store behavior has been dramatically attracted scholars and practitioners in past ten years. As a small RFID tag attached to the shopping carts can be recognized as surrogates instead of enumerators to trail the customers, it can provide us an objective and direct perspective to observe and measure the in-store behavior of customers. In this article, we present a study on this new type of in-store behavior data named RFID data, which can improve the understanding of purchase behavior of customers with emphasis on meaningful knowledge via analysis of RFID data. In contrast to prior studies in this research field, this paper has paid special attention to shopping time that customers spent in supermarket (so-called stay time), and presents methodological analysis into two folds. First, we develop a bayesian network (BN) model to combine both of purchase behavior and in-store behavior as features. As BN is a probabilistic graphical model, it can provide an quantitative analysis process of purchase behavior decision over stay time and also allow us to interpret the decision process of purchasing in a much more intuitive measurement. The results show BN has a better accuracy than other typical prediction models (linear discriminant analysis, logistic regression and support vector machine). Second, due to BN can estimate and predict in a nonlinear correlation between purchase intention and stay time, we examine a tedium effect on purchase behavior. During the customers wander in shopping, purchase intention represents a nonmonotonic phenomena accounting for the long stay time. Finally, we also investigate the sensitivity and specificity of purchase behavior predicted by our proposal in adjustment of decision threshold and implement several business decision-making implications in actual business situations.

    DOI: 10.1007/978-3-319-28379-1_5

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  • Why Do Retailers End Price Promotions: A Study on Duration and Profit Effects of Promotion Reviewed

    Zhen Li, Katsutoshi Yada

    2015 IEEE International Conference on Data Mining Workshop (ICDMW)   328 - 335   2015

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    Evidence shows that price promotion can help small and medium-sized retailers to increase their sales and profits. However, retailers usually stop the promotion after a certain duration. This study tries to explain why retailers discontinue a price promotion. Our approach assumes that overall contributions of price promotion to retailers' profits decrease progressively with time after the promotion begins. We propose simple econometric models to investigate the relationship between promotion duration and the overall effects of price discount on retailers' profits, by using point-of-sale data from Japan's supermarkets. The finding suggests that overall effects of price promotion on retailers' profits have a downward process with the elapsed time. We hope our paper could be helpful for marketers to understand the dynamic profits effects of price promotion, and to set optimal duration of promotions. The paper also discusses management implications and future research directions at the end.

    DOI: 10.1109/ICDMW.2015.56

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  • Evaluation of price elasticity and brand loyalty in milk products

    Natsuki Sano, Syusuke Tamura, Katsutoshi Yada, Tomomichi Suzuki

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014   35   1482 - 1487   2014

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:ELSEVIER SCIENCE BV  

    Basic necessities are generally said to be price inelastic in comparison with luxury goods. However, within the former group, it is not easy to differentiate between milk products using factors other price. Therefore, price could be an important factor when deciding between milk products. In this study, we verify the hypothesis that milk products are price elastic based on customer transaction data. If milk products are non-elastic in terms of price, we consider that people's choice of brand is decided by factors other than price, such as brand loyalty, which we then evaluate using a multinomial logit model. (C) 2014 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2014.08.213

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  • Using Bayesian Network for Purchase Behavior Prediction from RFID Data

    Yi Zuo, Katsutoshi Yada

    2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC)   pp.2262-2267   2262 - 2267   2014

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    This paper represents our recent studies about the prediction of purchase behavior and an advancement of in-store behavior with respect to RFID technology. In contrast to prior innovators in this research field, this paper has paid special attention to stay time spent on shopping in a target area rather than the whole supermarket, which can serve us to interpret the decision process of purchasing one product or a series of products in a much more intuitive and precise measurement. Also, we develop an integrated model to combine purchase behavior and in-store behavior. A probabilistic graphical model - bayesian network is employed to demonstrate a quantitative analysis process of purchase behavior decision over stay time. In order to distinguish purchase intention among different customers, an attitudinal factor - purchase background of customer is introduced in this paper to build bayesian network. As bayesian network can only deal with the discrete variables, a clustering algorithm is applied to discretize the continuous variables. In the experiments, the optimal cluster number of stay time and purchase background is examined for maximizing the performance evaluation with higher accuracy, and the results also show bayesian network has a better accuracy than other typical prediction models. Finally, we investigate the sensitivity and specificity of purchase behavior predicted by our proposal in adjustment of decision threshold, and use ROC (Receiver Operating Characteristic) curve to determine the optimal decision threshold which can maximize the classification accuracy of models.

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  • Category Evaluation Method for Business Intelligence Using a Hierarchical Bayes Model

    Natsuki Sano, Katsutoshi Yada, Tomomichi Suzuki

    2014 IEEE 13TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI-CC)   pp.400- 407   400 - 407   2014

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    Effective category management by grocery stores requires product category evaluation. Previous studies have evaluated product categories using point-of-sales shopping behavior data. Recent developments in radio frequency identificatio technology facilitate the tracking of customer shopping paths within a store and aggregated stay time in sales areas. In addition to radio frequency identificatio data, we use product discount information from discount flue data and construct a sales area evaluation model incorporating stay time and bargain scale in sales areas. We employ a hierarchical Bayes model to predict the number of purchased products. A typical hierarchical Bayes model considers each customer as a subject; however the proposed model considers each sales area as a subject. From estimation results of the posterior parameter, we evaluate the influenc of stay time and discount scale for each sales area. This provides useful performance measure for the sales areas.

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  • Consumer purchasing behavior extraction using statistical learning theory

    Yi Zuo, A. B. M. Shawkat Ali, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014   35   1464 - 1473   2014

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    Consumers classification is one of the most important task in the retail sector. RFID (Radio Frequency IDentification) - A wireless non-contact technology is made easier to classify the consumers' in-store behavior, recently. This paper presents an extraction of consumer purchasing behavior using statistical learning theory SVM (Support Vector Machine). In this research, we present our recent investigation outcome on the consumers shopping behavior in a Japanese supermarket using RFID data. We observe that it is possible to express the individual difference of consumers how are they spending time (we call it stay time in this paper) on shopping in a certain area of the supermarket. The contribution of this research is in two folds: we employ a SVM model on dealing with the RFID data of the consumer in-store behaviour firstly, as compared with other forecast model such as linear regression analysis and bayesian network, SVM provides a significant improvement in the forecasting accuracy of purchase behaviour (from 81.49% to 88.18%). Secondly, the kernel trick is adopted inside the SVM theory to choose the appropriate kernel for consumer purchasing behavior extraction. (C) 2014 The Authors. Published by Elsevier B.V.

    DOI: 10.1016/j.procs.2014.08.209

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  • A framework for analysis of the effect of time on shopping behavior

    Keiji Takai, Katsutoshi Yada

    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS   41 ( 1 )   91 - 107   2013.8

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    Due to technological developments, data about how many items a customer buys and how long the customer spends in a supermarket are available. A major problem with the data, however, is that there is no framework that considers the heterogeneity hidden in the data. In this article, we propose a framework that considers heterogeneity in the number of items a customer buys. The first step of our framework is based on the Poisson mixture regression model using a stationary time in the department where the items are sold as its independent variable. This model finds latent homogeneous groups of customers and gives the regression models within each group. It simultaneously classifies the customers into the homogeneous groups. In the second step of our framework, a method to investigate whether another factor (variable) influences the classification into homogeneous groups is presented. This proposed framework is applied to real data collected from the customers, and the effectiveness of the framework is shown. The managerial implications are drawn from the result of the analysis.

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  • Determining the Share of Product Categories on Discount Flyers Based on the Interaction Effect between Bargain Scale and Sales Area

    Natsuki Sano, Katsutoshi Yada

    PROCEEDINGS OF THE 2013 12TH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI CC 2013)   pp.315-319   315 - 319   2013

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    In grocery stores, discount flyers act as an important tool to provide discount information to customers and spur buying motivation among them. However, discount flyer have limited space. As customers purchase popular products even if they are not actively promoted, mentioning them on discount flyers is an ineffective strategy. Therefore, the proper allocation of discounted products is an important topic which is related to category management. In this paper, we propose a prediction model for the number of purchased products based on the data obtained through radio frequency identification (RFID), point of sales (POS), and discount flyers. We propose a method to determine the share of product categories on discount flyers by evaluating the interaction effect between sales area and bargain scale using the prediction model. The experimental results show that the sales area near the register has a significant interaction effect on bargain sales, suggesting that discounted products should be arranged in front of the register.

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  • Application of Bayesian Network Sheds Light on Purchase Decision Process basing on RFID Technology Reviewed

    Yi Zuo, Katsutoshi Yada

    2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)   242 - 249   2013

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    Recently, a wireless non-contact technology named RFID(Radio Frequency IDentification) has brought a new perspective on process of purchase decision. Via the RFID tag attached to a shopping cart, the position information of customers in a grocery store can be captured every moment This paper presents our study based on this type of data. In this study, we transform the RFID data into stay time that the customers spend in supermarket until purchase is decided. In addition to RFID data, a purchase transaction based on POS(Point of Sale) data that only represent the point when customers come to purchase can be extended to a process of in-store behavior. As a probabilistic graphical model named bayesian network is employed to applied, we investigate the stay time how to affect the purchase probability. In the experiment, we also reveal that this affect is different during the purchase decision process by customers in individual age bracket. Moreover, numerical results show our proposal has a better accuracy than other models such as logistic regression analysis.

    DOI: 10.1109/ICDMW.2013.68

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  • Analyzing consumers' shopping behavior using RFID data and pattern mining

    Takanobu Nakahara, Katsutoshi Yada

    ADVANCES IN DATA ANALYSIS AND CLASSIFICATION   6 ( 4 )   355 - 365   2012.12

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    The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer's position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.

    DOI: 10.1007/s11634-012-0117-z

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  • Customer behavior modelling using radio frequency identification data and the hidden markov model

    Katsutoshi Yada, Natsuki Sano

    Annual SRII Global Conference, SRII   pp.509-514   509 - 514   2012

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    Developments in radio frequency identification (RFID) technology have made data on customer movement paths in supermarkets available. In this paper, we propose a method for customer behavior modeling by using RFID data and the hidden Markov model (HMM). In this method, "Stop" and "Pass by" behavior are estimated and the proposed method is evaluated by predicting the sales areas where customers actually purchased items. Using this method, we also demonstrate the shopping momentum. This effect, however, is experienced by only some customers, not all. © 2012 IEEE.

    DOI: 10.1109/SRII.2012.63

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  • An Examination of the Impact of Neurophysiologic and Environmental Variables on Shopping Behavior of Customers in a Grocery Store in Japan

    Marina Kholod, Katsutoshi Yada

    ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS   243   2099 - 2103   2012

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    Thanks to the advancement of tracking technologies such as RFID (Radio Frequency Identification) in recent years, the study area of shopping behavior of consumers in retailing setting has globally regained increased interest. However, only few studies have attempted to analyze consumer shopping behavior using the RFID data. In this paper we postulate the hypotheses which can be tested using RFID data. This paper aims to contribute to the understanding of the impact of neurophysiologic (human) and environmental (store layout) variables on walking and shopping behavior, by conducting the literature review related to orientation of customers in retailing space. It proposes a framework that addresses the relationship between two types of variables; and builds up three main hypotheses related to their different facets. Finally, the managerial implications are also proposed based on the developed framework.

    DOI: 10.3233/978-1-61499-105-2-2099

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  • The Influence of Sales Areas and Bargain Sales on Customer Behavior in the Grocery Store

    Natsuki Sano, Katsutoshi Yada

    ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS   243   2104 - 2113   2012

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    Because of developments in Radio Frequency Identification (RFID) technology, data about customers' movement paths in the grocery store are now available. In this paper, we propose a customer behavior model in the grocery store using RFID data. The proposed model is based on a nonhomogeneous hidden Markov model with covariates, and "Stop" and "Pass by" behaviors are estimated. The purchase probability of each behavior is confirmed, and we find that customers often purchase with high probability when the behavior is "Stop." In addition, we examine the influence of sales areas and bargain sales on customer behavior by using these variables as covariates. We find that certain sales areas have strong effects on the "Stop" behavior. We confirm that these sales areas are arranged along the perimeter of the store or in the central aisle; the obtained knowledge corresponds to empirical knowledge.

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  • International workshop on innovating service systems

    Yukio Ohsawa, Katsutoshi Yada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6797 LNAI   232   2011.11

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    Service science has been raised, and coming to be established as a research domain all over the world. This workshop in Tokyo has been motivated by the systems-design dimension of the service science. We aim to share and discuss a progressive vision to develop methods for innovating systems of service resources where novel values are created and supplied sustainably. A "service system" here is an artificially organized or self-organized active integration of the following resources: 1 Participants, i.e., providers and consumers of services, where a provider of a service may turn into a consumer in different contexts 2 Money, or other entities representing value, and their active flows: An utterance of praise can be also this kind of entity 3 Supply chains i.e., the chain of interactions from creators of service resources (products, information, food, etc) to consumers of services, and 4 Tools (computers, robots, sensing devices, etc) aiding the activities of (1), (2), and (3) In this workshop, we discussed methods for designing and realizing service systems and parts of a service system, with positioning resources of the four kinds above in the systems to be created. By this, we aim to respond to the social demand to design an environment for value-creative and dynamic interactions among participants via resources in the market, rather than merely passing existing products and services from providers to customers for predetermined prices. © 2011 Springer-Verlag.

    DOI: 10.1007/978-3-642-25655-4_21

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  • String analysis technique for shopping path in a supermarket

    Katsutoshi Yada

    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS   36 ( 3 )   385 - 402   2011.6

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    The sensor network technology developed in recent years has made it possible to accurately track the in-store behavior of customers which was previously indeterminable. The information on the in-store behavior of customers obtained by using this technology, namely information on their shopping path, provides us with useful information concerning the customer's purchasing behavior. The purpose of this research is to apply character string analysis techniques to shopping path data so as to analyze customers' in-store behavior, and thereby clarify technical problems with them (the character string analysis techniques) as well as their usability. In this paper we generated character strings on visit patterns to store sections by focusing exclusively on customers stopping by these sections in order to elucidate the visiting patterns of customers who made a large quantity of purchases. In this paper, we were able to discover useful information by using the character string analysis technique EBONSAI, thereby illustrating the usability and usefulness of character string analysis techniques in shopping path analysis.

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  • Clockwise and anti-clockwise directions of customer orientation in a supermarket: Evidence from RFID data

    Marina Kholod, Keiji Takai, Katsutoshi Yada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 )   304 - 309   2011

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    Customer orientation is one of the important yet underresearched topics in the retailing management. In this paper we replicate and extend research by Groeppel-Klein and Bartmann (2008), analyzing the new type of data, namely RFID (Radio Frequency Identification) data, with the purpose to examine grocery shoppers' moving direction within the store and its influence on their buying behavior. As a result, we found out that attributes of shopping process, such as purchases and walking distance, vary significantly, depending on shoppers' clockwise or anti-clockwise moving direction. As retailers would benefit from knowledge about how the moving direction of customers relate to the buying behavior, managerial implications are proposed. © 2011 Springer-Verlag.

    DOI: 10.1007/978-3-642-23854-3_32

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  • Analysis of residence time in shopping using RFID Data an application of the Kernel density estimation to RFID - An application of the Kernel density estimation to RFID

    Shinya Miyazaki, Takashi Washio, Katsutoshi Yada

    Proceedings - IEEE International Conference on Data Mining, ICDM   pp.1170-1176   1170 - 1176   2011

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    This study shows a method of determining and visualizing the existence probability of customers from shopping-path data in supermarkets using a database collected by a RFID (Radio Frequency Identification) technique, which allows us to analyze the detailed behaviors of customers. First, we present a method to estimate customer existence probability density on the sales floor using a Kernel density estimation. The kernel density estimation obtains continuous distribution of the existence probability density and grasps the detailed movements of customers. This estimation is better than an aggregation of residence time in each sales floor zone. Secondly, we visualize the customer existence probability density as cartographic output. Thirdly, we assess the relations between customer existence probability and sales in each sales floor zone using both shopping-path data and customer purchasing records. Finally, we assess the relation between customer existence probability and sales on each store shelf to verify the utility of this method for sales and marketing. © 2011 IEEE.

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  • Extraction of customer potential value using unpurchased items and in-store movements

    Takanobu Nakahara, Katsutoshi Yada

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6883 ( 3 )   295 - 303   2011

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    RFID data obtained from customers' movements using radio-frequency identification (RFID) tags contain valuable information for marketing, such as shopping trip time and distance as well as the number of shelf visits. Customers' purchasing behavior and their in-store movements can be analyzed not only by using RFID data, but also by combining it with point of sales (POS) data. In this paper, we propose an index called customer potential value (CPV), which considers customers' potential for purchasing products using association rules and visited shelves, by analyzing the RFID and POS data. Finally, we use CPV for a purchase prediction of products and a method of customers' segmentation of a sales promotion. © 2011 Springer-Verlag.

    DOI: 10.1007/978-3-642-23854-3_31

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  • Shopping path modeling using a transition matrix compression algorithm

    Xiaojun Ding, Katsutoshi Yada

    Studies in Computational Intelligence   369   329 - 339   2011

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    Studying shopping behavior is an important and interesting topic for researchers and practitioners. With the improvement of technology in data collection and handling, it is possible and important to take full advantage of these data opportunities to analyze in-store shoppers' movements so as to understand shopping behavior from different standpoints. In this study, we set up a three-component procedure-based application with the use of a method from Markov chain approach. In this procedure, the sensor network system, the Shopper In-store Movement Graph (SIMG) generator, and the Transition Matrix Compression Algorithm (TMCA) engine, are used to analyze in-store shopping paths and to cluster in-store zones with similar transition behaviors. After clustering, some characteristics of complicated shoppers' movements can be observed more clearly. An experiment is performed on real data to illustrate that the procedure works in practice. Finally, we close with a brief conclusion and an outlook for the future. © 2011 Springer-Verlag Berlin Heidelberg.

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  • Decision support system for policy making during a financial crisis

    Katsutoshi Yada, Kohei Ichikawa

    Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011   pp.756-760   756 - 760   2011

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    This paper introduces an application of the data mining tool, MUSASHI (Mining Utilities and System Architecture for Scalable processing of Historical data), for scientific policy making. Recent advances in information systems have allowed researchers to gather enormous amounts of data on opinions about government policy. However, these gathered data have been individually stored, and have never been integrated because of a lack of techniques to analyze the data in an integrated way. In this paper we propose a cloud application for policymaking during a financial crisis. For this purpose, we have developed an ASP- platform- leveraging distributed- computing technology represented by Cloud computing. © 2011 IEEE.

    DOI: 10.1109/GRC.2011.6122693

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  • Evaluation of the Shopping Path to Distinguish Customers Using a RFID Dataset

    T. Nakahara, K. Yada

    International Journal of Organizational and Collective Intelligence   Vol.2, No.4, pp.1-14   2011

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  • Finding Latent Groups of Customers via the Poisson Mixture Regression Model

    Keiji Takai, Katsutoshi Yada

    2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)   pp.3603-3608   3603 - 3608   2011

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    Due to developments in technology, movement data tracking a customer's movements in a supermarket in addition to conventional POS data are now available. A problem in analyzing such data is that an ordinary statistical model assuming customer homogeneity does not fit well to such data. In this article, we propose a framework for analyzing such data in a collection of supermarket departments. The framework is based on the mixture regression model assuming the customers' heterogeneity. By the model, we find the latent homogenous groups of the customers and explain the number of items by a stationary time based on the regression model in each latent group. The method of the mixture regression model is explained in addition to the estimation method. We found that a small number of the customers buy more items by going to the supermarket departments and are more sensitive to the stationary time, while a large number of the customers buy less and are less sensitive.

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  • Relation between Stay-Time and Purchase Probability Based on RFID Data in a Japanese Supermarket

    Keiji Takai, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III   6278   254 - 263   2010

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    Radio Frequency Identification (RFID) technology uses radio waves to track an object to which a small tag is attached. In a Japanese supermarket, we attach the RFID device to the cart and collect data on purchase behavior. In this article, we clarify the relation between purchase probability and the time customers spend in the store section by analyzing the RFID data with main use of descriptive methods. We clarify the way how the stay-time explains the purchase probability and characteristics of each store section. Based on the result, some implications for business are made as well.

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  • Extracting Promising Sequential Patterns from RFID Data Using the LCM Sequence

    Takanobu Nakahara, Takeaki Uno, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III   6278   244 - +   2010

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    Recently, supermarkets have been using RFID tags attached to shopping carts to track customers' in-store movements and to collect data on their paths. Path data obtained from customers' movements recorded in a spatial configuration contain valuable information for marketing. Customers purchase behavior and their in-store movements can be analyzed not only by using path data but also by combining it with POS data. However, the volume of path data is very large, since the position of a cart is updated every second. Therefore, an efficient algorithm must be used to handle these data. In this paper, we apply LCMseq to shopping path data to extract promising sequential patterns with the purpose of comparing prime customers' in-store movements with those of general customers. LCMseq is an efficient algorithm for enumerating all frequent sequence patterns. Finally, we construct a decision tree model using the extracted patterns to determine prime customers' in-store movements.

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  • Development of data mining platform MUSASHI towards service computing

    Kohei Ichikawa, Katsutoshi Yada, Takashi Washio

    Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010   pp.235-240   235 - 240   2010

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    The objective of this paper is to introduce further development of a data mining tool, MUSASHI (Mining Utilities and System Architecture for Scalable processing of HIstorical data), for service computing. Recent advances in information systems have allowed us to gather enormous amounts of data on marketing. However, these gathered data have been individually stored at each company, and have never been integrated because of a lack of techniques to analyze the data in an integrated way and to handle the large amount of data efficiently. To address this issue, we are currently investigating a way to provide a data mining platform as a service so that users can apply various data mining techniques to their marketing data with ease and at a low cost. For this purpose, we have developed an ASP platform leveraging distributed computing technology rep- resented by Cloud computing. This paper describes the ASP platform for data mining services and introduces an empirical application of data mining using our platform. © 2010 IEEE.

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  • Modeling deposit outflow in financial crises: application to branch management and customer relationship management

    K. Yada, T. Washio, Y. Ukai

    International Journal of Advanced Intelligence Paradigms   Vol.2, No.2/3, pp.254-270 ( 2/3 )   254 - 254   2010

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    DOI: 10.1504/ijaip.2010.030538

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  • Modelling deposit outflow in financial crises: Application to branch management and customer relationship management

    Katsutoshi Yada, Takashi Washio, Yasuharu Ukai

    International Journal of Advanced Intelligence Paradigms   2 ( 2-3 )   254 - 270   2010

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    This paper aims to propose models for deposit outflows caused by various financial crises, and to present a framework of knowledge discovery required for bank management to create branch strategies and customer strategies for financial crises. Based on the models proposed in this paper, we understand effects on consumer behaviour caused by various financial crises (computer system failure, scandal, natural disaster, etc.), and estimate the total amount of deposit outflows, based on consumer behaviour data acquired through questionnaires to consumers. A bank can create appropriate branch strategies and customer strategies according to the knowledge obtained from these models. Copyright © 2010 Inderscience Enterprises Ltd.

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  • The Influence of Shopping Path Length on Purchase Behavior in Grocery Store

    Marina Kholod, Takanobu Nakahara, Haruka Azuma, Katsutoshi Yada

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III   6278   273 - +   2010

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    In this paper we analyze the new type of information, namely RFID (Radio Frequency Identification) data, collected from the experiment in one of the supermarkets in Japan in 2009. This new type of data allows us to capture different aspects of actual in-store behavior of a customer, e. g. the length of her shopping path. The purpose of this paper is to examine more closely the effect of shopping path length on sales volume, which is one of the established ideas in RFID research as well as in retailing industry. In this paper we developed a simple framework, based on criteria of Wandering Degree and Purchase Sensitivity, in order to see how the relationship between distance* walked within the store and sales volume interacts with walking behavior of customers. As a result, in this paper we came up with some useful suggestions for more efficient in-store area management.

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  • Advertising Carryover Effects and Optimal Budget Allocation Reviewed

    YADA Katsutoshi, K. Ichikawa, N. Nakachi, T. Washio

    Proc. of KES 2009   LNAI 5712, pp.270-277   2009.9

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  • Advertising Carryover Effects and Optimal Budget Allocation

    K. Ichikawa, K.Yada, N. Nakachi, T. Washio

    Proc. of KES 2009, LNAI 5712   pp.270-277   2009.9

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  • Special issue on chance discovery - Discovery of significant events for decision making

    Yukio Ohsawa, Katsutoshi Yada

    INFORMATION SCIENCES   179 ( 11 )   1567 - 1569   2009.5

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    Language:English   Publisher:ELSEVIER SCIENCE INC  

    DOI: 10.1016/j.ins.2008.12.007

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  • String Analysis Technique for Shopping Path in a Supermarket

    YADA Katsutoshi

    Journal of Intelligent Information Systems   36 ( 3 )   2009

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  • Optimization of Budget Allocation for TV Advertising Reviewed

    Kohei Ichikawa, Katsutoshi Yada, Namiko Nakachi, Takashi Washio

    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS   5712   270 - +   2009

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    This research aims to present an analysis to optimally allocate advertising budgets based on single source data on consumers' views of TV advertising. A model of consumer behavior and an optimality criterion for the advertising budget allocation are proposed together with a CA based optimization algorithm. Through the analysis, we discovered some knowledge to improve the effectiveness of advertising for several products.

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  • Data Mining for Design and Marketing Reviewed

    YADA Katsutoshi, Y.Ohsawa (eds.), K.Yada (eds.)

    CRC Press   2009

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  • Modeling Bank Runs in Financial Crises Reviewed

    K.Yada, T.Washio, Y.Ukai, H.Nagaoka

    The Review of Socionetwork Strategies   Vol.3, No.1, pp.19-31 ( 2 )   19 - 31   2009

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  • A bank run model in financial crises

    Katsutoshi Yada, Takashi Washio, Yasuharu Ukai, Hisao Nagaoka

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS   5178   703 - +   2008

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    Financial crises are occurring frequently in Asia, Europe, and America, and it is important for banks to investigate strategies for such crises. The objective of this research is to build a model for bank runs by deposit holders in financial crises, and use that model to present a framework for estimating the amounts of deposit withdrawals during Financial crises. By carrying out detailed investigation of the bank run model thus constructed, we clarified that the characteristics of customers and branch locations both bring about differences in bank runs. Our estimated amounts of deposit withdrawals during financial crises Suggest that each branch should adopt a Customer strategy appropriate for the variety of customers of that branch. The bank run model proposed in this research can also be applied to other marketing strategy planning, and has wide applicability.

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  • Character string analysis and customer path in stream data

    Katsutoshi Yada

    Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008   pp.829-836   829 - 836   2008

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    This purpose of this study is to propose a knowledge-discovery system that can abstract helpful information from character strings representing shopper visits to product sections associated with positive and negative purchasing events by applying character string parsing technologies to stream data describing customer purchasing behavior inside a store. Taking data that traced customers' movements we focus on the number of times customers stop by particular product sections, and by representing those visits in the form of character strings, we propose a way to efficiently handle large stream data. During our experiment, we abstract store-section visiting patterns that characterize customers who purchase a relatively larger volume of items, and are able to show the usefulness of these visiting patterns. In addition, we examine index functions, calculation time, and prediction accuracy, and clarify technological issues warranting further research. In the present study, we demonstrate the feasibility of employing stream data in the marketing field and the usefulness of the employing character parsing techniques. © 2008 IEEE.

    DOI: 10.1109/ICDMW.2008.41

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  • Pricing System for Seeking Optimal Prices in the Diet Foods Market

    Kosuke Ohno, Katsutoshi Yada

    2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6   pp.3514-3518   3513 - 3517   2008

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    The purpose of this study is to introduce a case study on the application of data mining technology to the matter of pricing in business, and to clarify the latent risks contained in that process. In this paper, we have used data mining technology to analyze the purchase history data of customers for the purpose of discovering the price pattern that maximizes store profits. We describe the processes from data analysis to implementing a business plan, and we consider the risks involved in the business application of data mining.

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  • Is this brand ephemeral? A multivariate tree-based decision analysis of new product sustainability

    Katsutoshi Yada, Edward Ip, Naoki Katoh

    DECISION SUPPORT SYSTEMS   44 ( 1 )   223 - 234   2007.11

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    Decision tree methodology has become an increasingly important tool set in the field of decision science. We develop a multivariate, tree-based decision system for a new application: the determination of whether a newly launched consumer product should be allowed to continue in a highly competitive market. The system is designed to overcome a shortcoming-the inability to capture multivariate interactions-of traditional decision methods. We apply the proposed method to an instant noodle sales data set that contains 38 million transactions, and compare results across several methods. (C) 2007 Elsevier B.V. All rights reserved.

    DOI: 10.1016/j.dss.2007.03.014

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  • CODIRO: A new system for obtaining data concerning consumer behavior based on data factors of high interest determined by the analyst Reviewed

    Katsutoshi Yada

    SOFT COMPUTING   11 ( 8 )   811 - 817   2007.6

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    The aim of this paper is to propose a new system for the strategic use of customer data that includes and integrates such differing data sources as company databases, mobile telephone networks and Internet data and is a consumer research support system for the discovery of new marketing opportunities. This system, called CODIRO, will be discussed in this paper using a case study of the effects on sales of processed food product television commercials. A system for verifying the validity of consumer behavior models will also be described and discussed. Use of the CODIRO analysis system makes it easy to introduce, into the analytic model, consumer attitude changes and in-store data of many types that have not been used to measure advertising and promotional activity effectiveness in the past.

    DOI: 10.1007/s00500-006-0123-1

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  • Data Mining Technique for Gene Analysis Makes Profits in the Supermarket Reviewed

    YADA Katsutoshi, Y. Hamuro, N. Katoh

    2007 AMA Winter Educators' Conference Proceedings   pp. 122-129   2007.2

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  • Lessons learned from a case study on process data management

    Katsutoshi Yada

    2007 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-8   pp.3530-3534   3797 - 3801   2007

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    In this research, we discuss the business application of process data, being the massive accumulated time series of changing conditions. Unlike the data resulting from daily operations, process data includes rich information on operational processes. It is unstructured and becomes massive in scale. In this paper, we introduce a project in which we participated, designed to utilize such process data in business. We also examine the lessons learned from this case study.

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  • Business Application and Risk of Data Mining Reviewed

    YADA Katsutoshi, D. Naito, K. Ohno

    Proc. of International Workshop on Risk Informatics (RI2007)   pp.21-29   2007

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  • Analysis on a relation between enterprise profit and financial state by using data mining techniques

    Takashi Washio, Yasuo Shinnou, Katsutoshi Yada, Hiroshi Motoda, Takashi Okada

    NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE   4384   305 - 316   2007

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    The knowledge on the relation between a financial state of an enterprise and its future profit will efficiently and securely reduce the negative risk and increase the positive risk on the decision making needed in the management of the enterprise and the investment in stock markets. Generally speaking, the relation is considered to have a highly complicated structure containing the influences from various financial factors characterizing the enterprise. Recent development of data mining techniques has significantly extended the power to model such a complicated relation in accurate and tractable manners. In this study, we assessed the feasibility to model the relation in the framework of data mining, and analyzed the characteristics of the model.

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  • Knowledge discovery from click stream data and effective site management

    Katsutoshi Yada, Kosuke Ohno

    NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE   4384   360 - 373   2007

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    The aim of this paper is to discuss the development of a system for the discovery of valuable new knowledge and to create effective sales strategies based on that knowledge by using massive amounts of click stream data generated by site visitors. This paper discusses and clarifies the process as to how detailed consumer behavior patterns are extracted from click stream data of Internet mall retail site and how such patterns can be used as a source of new ideas for creating new marketing strategies. We will also discuss our successful use of an improved version of the genome analysis system called E-BONSAI to extract and analyze special character strings related to site visitor behavior indicated by distinctive click patterns.

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  • 説得プロセス分析の枠組みと債権回収会話ログへの適用 Reviewed

    矢田 勝俊, 砂山 渡

    人工知能学会論文誌   Vol.22, No.2, pp.239-247   2007

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  • Analysis framework for persuading process and an application to debt-collecting conversation logs Reviewed

    Wataru Sunayama, Katsutoshi Yada

    Transactions of the Japanese Society for Artificial Intelligence   22 ( 2 )   239 - 247   2007

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    The purpose of this research is to develop a framework to analyze the content and a process of persuading process and its application to the communication for the debt-collecting process. It is possible for us to understand how the skilled workers have used the keyword groups concerning the motivation to pay, the payment methods and the payment confirmation in their conversations, to model a persuading process. There is no research and method to deal with a large amount of conversation logs for discovering useful knowledge about a persuading process. In this paper, we were successful in discovering a part of the distinctive features of skilled workers in their conversations for the overdue payment collection, applying our method to communication data in a Japanese telecommunications company.

    DOI: 10.1527/tjsai.22.239

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  • 店舗利益最大化のための最適プライシングシステム―PRISMの理論と実践―

    矢田 勝俊

    『情報技術の産業応用フロンティア』研究双書, 関西大学経済・政治研究所   第141冊, pp.121-146   2006

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  • Knowledge discovery from the structure of persuasive communication

    Katsutoshi Yada, Naohiro Matsumura

    2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS   pp.1741-1746   1741 - +   2006

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    It is difficult to carry out quantitative measurements of the persuasive power of business communications (i.e., persuasive skills) and such communications are likely to involve difficult to understand, unseen and unknown knowledge. However, using unstructured recorded communication data based on conversations with business customers, we have been developing explicit knowledge concerning skills necessary for effective communications in the form of an expression framework. The objective of this research is to generate a framework and a process for explicit management knowledge concerning understandable communication skills as opposed to the tacit, hard to understand the negotiating skills related to overdue payment collection personnel and to verify the actual usefulness of this knowledge using the accumulated data in a company. Using this process we have developed, it is possible to discover the special characteristics of the communication content of high success overdue payment collection personnel.

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  • PRISMを用いた店舗利益最大化のためのプライシング戦略

    矢田 勝俊

    オフィス・オートメーション学会誌(A)   Vol.26, No.3, pp.91-97   2006

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  • Does Web Log Data Reveal Consumer Behavior? The Case of Analysis for an Internet Mall Reviewed

    YADA Katsutoshi, N.Matsumura, K.Ohno, H.Tamura

    Proc. of the Annual Conference of the Academy of Marketing Science, Academy of Marketing Science   Vol.24, pp.256-262   2006

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  • The practice of an optimal pricing strategy for maximizing store profits using PRISM

    Katsutoshi Yada, Kosuke Ohno

    2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS   pp.2121-2126   2121 - +   2006

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    The purpose of this paper is to introduce a process for implementing optimal pricing that uses PRISM to maximize store profits. PRISM is a system and process that uses data mining technology to process large volumes of data, then develops a probability model for customer purchases, and which then uses a heuristic approach to identify the pricing pattern that will maximize store profits. For this paper, we used customer purchase data from Japanese supermarkets to identify the optimal pricing pattern for curry roux, which would maximize store profits.

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  • The river-rafting system for knowledge discovery related to persuasion process conversation logs

    Wataru Sunayama, Katsutoshi Yada

    ICDM 2006: Sixth IEEE International Conference on Data Mining, Workshops   pp. 575-579   575 - 579   2006

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    The purpose of this research is to develop a framework to represent the content and process of persuasion communications for overdue payment collection, thus making it possible to examine how the skilled operators have used theme related keywords concerning motivations to pay, the payment methods and the payment confirmation in their negotiation to achieve higher collection success. This paper describes a basis for modeling a persuasion process. There has been no research or methods for dealing with large amounts of conversation logs for discovering useful knowledge about persuasion processes. In this paper, we report our successful efforts in discovering a part of the distinctive features of skilled worker techniques as indicated in their conversations related to overdue payment collection and the application of our methods to communication data related to a Japanese telecommunications company.

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  • The structure of scenario communication and chance discovery

    Katsutoshi Yada

    Studies in Computational Intelligence   30   21 - 36   2006

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    DOI: 10.1007/978-3-540-34353-0_2

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  • A Data Mining for Graph Structure Data Helps to Discover New Knowledge in Consumer Behavior and Makes Profits Reviewed

    YADA Katsutoshi, H.Motoda, T.Washio

    Proc. CD of AMS International Retailing Conference 2005, pp.1-17   2005.6

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  • Active Mining for Structured Data Reviewed

    MOTODA Hiroshi, HO Tu Bao, WASHIO Takashi, YADA Katsutoshi, YOSHIDA Tetsuya, OHARA Kouzou, Hiroshi Motoda, Tu Bao Ho, Takashi Washio, Katsutoshi Yada, Tetsuya Yoshida, Kouzou Ohara, The Institute of Scientific and Industrial Research Osaka University, School of Knowledge Science Japan Advanced Institute of Science and Technology, The Institute of Scientific and Industrial Research Osaka University, Faculty of Commerce Kansai University, Graduate School of Information Science and Technology Hokkaido University, The Institute of Scientific and Industrial Research Osaka University

    Journal of Japanese Society for Artificial Intelligence   第20巻2号 ( 2 )   172 - 179   2005.3

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    Grant-in-Aid for Scientific Research on Priority Areas

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    Other Link: http://id.nii.ac.jp/1004/00006204/

  • Chance Discovery from Consumer Research Using KeyGraph

    YADA Katsutoshi, T.Araki, S.Hamada, N.Matsumura, S.Niwase, Y.Ohsawa

    Readings in Chance Discovery, Advanced Knowledge International   2005.2

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  • Data mining oriented CRM systems based on MUSASHI: C-MUSASHI

    K Yada, Y Hamuro, N Katoh, T Washio, Fusamoto, I, D Fujishima, T Ikeda

    ACTIVE MINING   3430   152 - 173   2005

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    MUSASHI is a set of commands which enables us to efficiently execute various types of data manipulations in a flexible manner, mainly aiming at data processing of huge amount of data required for data mining. Data format which MUSASHI can deal with is either an XML table written in XML or plain text file with table structure. In this paper we shall present a business application system of MUSASHI, called C-MUSASHI, dedicated to CRM oriented systems. Integrating a large amount of customer purchase histories in XML databases with the marketing tools and data mining technology based on MUSASHI, C-MUSASHI offers various basic tools for customer analysis and store management based on which data mining oriented CRM systems can be developed at extremely low cost. We apply C-MUSASHI to supermarkets and drugstores in Japan to discover useful knowledge for their marketing strategy and present possibility to construct useful CRM systems at extremely low cost by introducing MUSASHI.

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  • CODIRO: A new system for obtaining data concerning consumer behavior based on data factors of high interest determined by the analyst

    K Yada

    Soft Computing as Transdisciplinary Science and Technology   Vol.11, No.8, pp. 811-817   511 - 520   2005

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    The aim of this paper is to propose a new system for the strategic use of customer data that includes and integrates such differing data sources as company databases, mobile telephone networks and Internet data and is a consumer research support system for the discovery of new marketing opportunities. This system, called CODIRO, will be discussed in this paper using a case study of the effects on sales of processed food product television commercials. A system for verifying the validity of consumer behavior models will also be described and discussed. Use of the CODIRO analysis system makes it easy to introduce, into the analytic model, consumer attitude changes and in-store data of many types that have not been used to measure advertising and promotional activity effectiveness in the past.

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  • Optimum pricing strategy for maximization of profits and chance discovery

    K Yamamoto, K Yada

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS   3681   1160 - 1166   2005

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    The objective of this paper is to present and discuss methods for formulating optimum pricing strategies for maximizing store profit levels using consumer purchase data. In order to maximize outlet profit levels, it is necessary to seek such pricing strategies after achieving an in-depth understanding of the various features of consumer purchase behavior concerning a wide range of products and their related prices. We authors have used a very large amount of consumer purchase behavior data in order to clarify the effects on store-level profits of a wide range of specific products to develop a support system for pricing specialists. At the end of this paper, we analyze the framework that has been presented for the system from the standpoint of its utility for discovery of new business chances and discuss the need to clarify the problems connected with the use of data mining-based pricing strategies.

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  • The future direction of new computing environment for exabyte data in the business world

    K Yada, Y Hamuro, N Katoh, K Kishiya

    2005 SYMPOSIUM ON APPLICATIONS AND THE INTERNET WORKSHOPS, PROCEEDINGS   316 - 319   2005

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE COMPUTER SOC  

    With the rapid spread of the Internet and the computerization of trading a huge amount of data on the Internet and of transaction database in enterprises has been accumulated. The purpose of this paper is to explain the significance of the technology to process of exabyte-scale data and presents the business application, CODIRO, which will make it possible to integrate various types of large scale data. CODIRO is a consumer research system which discovers new knowledge by integrating the huge amount of different types of data both on the Internet and within companies. This paper will demonstrate the business implications for exabyte-scale information technology research, by explaining an example of the analysis of the sales effectiveness of television commercials using CODIRO.

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  • Consumer behavior analysis by graph mining technique

    K Yada, H Motoda, T Washio, A Miyawaki

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS   3214   800 - 806   2004

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    In this paper we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.

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  • Knowledge Discovery Process and Introduction of Domain Knowledge

    YADA Katsutoshi

    IRM Press   pp.86-98   2004

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  • Consumer behavior analysis by graph mining technique

    K Yada, H Motoda, T Washio, A Miyawaki

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS   3214   800 - 806   2004

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    In this paper we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.

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  • Business application for sales transaction data by using genome analysis technology

    N Katoh, K Yada, Y Hamuro

    DISCOVERY SCIENCE, PROCEEDINGS   2843   208 - 219   2003

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    We have recently developed an E-BONSAI (Extended BONSAI) for discovering useful knowledge from time-series purchase transaction data, developed by improving and adding new features to a machine learning algorithm for analyzing string pattern such amino acid sequence, BONSAI, proposed by Shimozono et al. in 1994. E-BONSAI we developed can create a good decision tree to classify positive and negative data for records whose attributes are either numerical, categorical or string patterns while other methods such as C5.0 and CART cannot deal with string patterns directly. We shall demonstrate advantages of E-BONSAI over existing methods for forecasting future demands by applying the methods to real business data. To demonstrate an advantage of E-BONSAI for business application, it is significant to evaluate it from the two perspectives. The first is the objective and technical perspective such as the prediction accuracy. The second is the management perspective such as the interpreterability to create new business action. Applying the E-BONSAI to forecast how long new products survive in instant noodle market in Japan, we have succeeded in attaining high prediction ability and discovering useful knowledge for domain experts.

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  • ビジネスにおけるチャンス発見の考え方

    矢田 勝俊

    東京電機大学出版局・チャンス発見の情報技術   29-42   2003

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  • Combining Information Fusion with String Pattern Analysis: A New Method for Predicting Future Purchase Behavior Reviewed

    YADA Katsutoshi, Yukinobu Hamuro, Naoki Katoh, Eddie Ip

    Springer, Information Fusion in Data Mining, Studies in Fuzziness and Soft Computing   Vol.123, pp.161-187   2003

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  • Knowledge discovery process from sales data

    K Yada

    INFORMATION TECHNOLOGY AND ORGANIZATIONS: TRENDS, ISSUES, CHALLENGES AND SOLUTIONS, VOLS 1 AND 2   pp.684-687   684 - 687   2003

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IDEA GROUP PUBLISHING  

    This paper describes the framework of knowledge discovery process in sales data and how the active mining system is applied to the data in the real business world by using the domain knowledge. First the framework of the knowledge discovery process in database is reviewed. It is not clear how users construct actual data mining process and use the domain knowledge in the existing model. We propose two-dimensional matrix of knowledge for sales data analysis to understand knowledge discovery process from purchase history. We distinguish data mining process from creation of business action. We point out that efficient knowledge discovery can be achieved by intensively introducing domain knowledge of experts to the creation of business action.

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  • MUSASHI:Flexible and Efficient Data Preprocessing Tool for KDD based on XML Reviewed

    YADA Katsutoshi

    Proceedings of the First International Workshop on Data Cleaning and Preprocessing   pp.38-49   2002.12

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  • A Neural Network Application to Identify High-Value Customer for a Large Retail Store in Japan

    YADA Katsutoshi

    Idea Group Publishing   55頁-69頁   55 - 69   2002

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  • Effective coupon distribution method that used client data

    YADA Katsutoshi

    Tokyo economic information publication   91頁-106頁   2001

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  • A Machine Learning Algorithm for Analyzing String Patterns Helps to Discover Simple and Interpretable Business Rules from Purchase History Reviewed

    YADA Katsutoshi

    Springer-Verlag, Progresses in Discovery Science   pp.565-575   2001

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  • Early detection of the ROYAL customer from a new client

    YADA Katsutoshi

    ESTRELA   89号10頁-17頁   2001

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  • Discovering association strength among brand loyalties from purchase history

    Y Hamuro, N Katoh, K Yada

    ISIE 2001: IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS PROCEEDINGS, VOLS I-III   114頁-117頁   114 - 117   2001

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    Analyzing purchase history of customers enables us to discover valuable knowledge that is helpful for developing effective sales promotion. In this respect, we shall introduce a new notion, association strength among brand loyalties, which is defined for every ordered pair of brands. If the association strength between loyalties of brands A and B is high, it represents that purchase of brand A is highly correlated to that of brand B. Conventional method for discovering associative purchasing is usually applied for one purchase opportunity (one receipt), i.e., it reveals how often two commodities are purchased at the same time, On the other hand, we are interested in discovering relationship among customers' loyalties to certain brands or manufacturers by investigating long-term purchase history of customers. By computing association strengths from customers' purchase history of drugstore chain in Japan, we could produce several interesting rules that will be useful for sales promotion planning.

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  • Knowledge discovery from management data Reviewed

    YADA Katsutoshi

    National economy magazine   第184巻第1号   2001

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  • A Data Mining System for Managing Customer Relationship

    YADA Katsutoshi

    101頁-105頁   2000.8

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  • The Discovery of Customer Loyalty from Newcomers

    YADA Katsutoshi

    185頁-189頁   2000.6

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  • Discovering Interpretable Rules that Explain Customer Brand Choice Behavior

    YADA Katsutoshi

    INFORMS-KORMS Seoul 2000   561頁-568頁   561 - 568   2000.6

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  • Data mining from the purchase record of a client

    YADA Katsutoshi

    169頁-178頁   167 - 178   2000

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  • The discovery of business chance from customer knowledge Reviewed

    K Yada, EH Ip, Y Hamuro, N Katoh

    IECON 2000: 26TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4   3   1638 - 1643   2000

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    The discovery of Loyal Customers at an early stage is extremely important in highly competitive market in order to increase business chance. Loyal Customer is defined as the customer who has brought high profit to the particular shop for a long period of time. Loyal Customer has been identified in the conventional approach by measuring sales volume or sales quantity for a certain period. However, such an approach is not effective to discover the Loyal Customer with high confidence and at an early stage. The purpose of this paper is to find an effective and robust rule to discover the future Loyal Customer from newcomers at an early stage with higher confidence, using the data-mining tool, C 5.0. The proposed approach is implemented by using real purchase data to observe the effectiveness.

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  • Approximation of optimal two-dimensional association rules for categorical attributes using semidefinite programming

    K Fujisawa, Y Hamuro, N Katoh, T Tokuyama, K Yada

    DISCOVERY SCIENCE, PROCEEDINGS   1721   148 - 159   1999

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    We consider the problem of finding two-dimensional association rules for categorical attributes. Suppose we have two conditional attributes A and B both of whose domains are categorical, and one binary target attribute whose domain is {"positive", " negative"}. We want to split the Cartesian product of domains of A and B into two subsets so that a certain objective function is optimized, i.e., we want to find a good segmentation of the domains of A and B. We consider in this paper the objective function that maximizes the confidence under the constraint of the upper bound of the support size. We first prove that the problem is NP-hard, and then propose an approximation algorithm based on semidefinite programming. In order to evaluate the effectiveness and efficiency of the proposed algorithm, we carry out computational experiments for problem instances generated by real sales data consisting of attributes whose domain size is a few hundreds at maximum. Approximation ratios of the solutions obtained measured by comparing solutions for semidefinite programming relaxation range from 76% to 95%. It is observed that the performance of generated association rules are significantly superior to that of one-dimensional rules.

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  • Mining pharmacy data helps to make profits

    Y Hamuro, N Katoh, Y Matsuda, K Yada

    DATA MINING AND KNOWLEDGE DISCOVERY   2 ( 4 )   391 - 398   1998.12

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:KLUWER ACADEMIC PUBL  

    Pharma, a drugstore chain in Japan, has been remarkably successful in the effective use of data mining. From over one tera bytes of sales data accumulated in databases, it has derived much interesting and useful knowledge that in turn has been applied to produce profits. Tn this paper, we shall explain several interesting cases of knowledge discovery at Pharma. We then discuss the innovative features of the data mining system developed in Pharma that led to meaningful knowledge discovery.

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  • Data mining oriented system for business applications

    Y Hamuro, N Katoh, K Yada

    DISCOVERY SCIENCE   1532   441 - 442   1998

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:SPRINGER-VERLAG BERLIN  

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  • The Future Direction of Data Mining Research

    YADA Katsutoshi

    JOURNAL OF OSAKA SANGYO UNIVERSITY Social Sciences   108号207頁-219頁   1998

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  • Customer Profiling Makes Profits : How did a Japanese firm achieve competitive advantage through the knowledge creation? Reviewed

    YADA Katsutoshi, Naoki Katoh, Yukinobu Hamuro, Yasuyuki Matsuda

    The Practical Application of Knowledge Management 98   pp.57-67. ( 109 )   247 - 256   1998

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  • Information Systems and Organizational Capabilities Reviewed

    YADA Katsutoshi

    Annual Report on Economics and Business Administration   46号105頁-158頁   105 - 158   1996

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    Language:Japanese   Publisher:Kobe University  

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Books

  • Advances in Artificial Intelligence Reviewed

    YADA,K, Ohsawa, Y, Ito, T, Takama, Y, Sato-Shimokawara, E, Abe, A, Mori, J, Matsumura, N( Role: Joint editor)

    Springer Nature  2020 

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  • 岩波データサイエンス vol.4

    矢田勝俊

    岩波書店  2016 

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  • Data Mining for Service

    K. Yada

    Springer-Verlag  2014 

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  • MUSASHI: System for Knowledge Discovery in Large Business Data

    YADA Katsutoshi, Yukinobu Hamuro, Naoki Katoh, Takashi Washio( Role: Joint author)

    Journal of the Japanese Society for Artifical Intelligence  2005.1 

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    Grant-in-Aid for Scientific Research on Priority Areas

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  • データマイニングと組織能力

    矢田 勝俊( Role: Sole author)

    多賀出版  2004 

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  • チャンス発見の情報技術

    矢田 勝俊, 大澤幸生( Role: Joint author)

    東京電機大学出版局  2003 

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  • データマイニング技術とビジネス応用

    矢田 勝俊, 原田保編著( Role: Sole author)

    日科技連, カスタマーマイニング  2003 

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    学部共同研究費

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MISC

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Presentations

  • The impact of self-control on search behavior

    YADA,Katsutoshi

    23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems  2019.9 

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    Event date: 2019.9

    Venue:Budapest, Hungary  

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  • Assessment of Effect of POP on Purchase Behavior: Comparison of Effectiveness of Eye-tracking Data and Shopping Path Data

    K. Ishibashi, K. Yada

    The 5th Asia-Pacific World Congress on Computer Science and Engineering 2018(APWC on CSE 2018)  2018.12 

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    Event date: 2018.12

    Venue:Fiji  

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  • Bayesian Hidden Markov Model for Evaluating the Influence of In-Store Stationary Time of Customers on their Purchase Behavior

    Y. Kaneko, K. Yada

    The 5th Asia-Pacific World Congress on Computer Science and Engineering 2018(APWC on CSE 2018)  2018.12 

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    Event date: 2018.12

    Venue:Fiji  

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  • How Game Users Consume Virtual Currency: The Relationship Between Consumed Quantity, Inventory, and Elapsed Time since Last Consumption in the Mobile Game World

    Y. Kaneko, K. Yada, W. Ihara, R. Odagiri

    ICDM DMS2018  2018.11 

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    Event date: 2018.11

    Venue:Singapore  

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  • An Empirical Study of the Relationship Among Self-Control, Price Promotions and Consumer Purchase Behavior

    X. Zhong, K. Ishibashi, K. Yada

    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  2018.10 

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    Event date: 2018.10

    Venue:Miyazaki Japan  

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  • The Short-Term Impact of an Item-Based Loyalty Program

    K. Yada, Y. Sun, B. Wu

    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  2018.10 

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    Event date: 2018.10

    Venue:Miyazaki Japan  

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  • Application of Network Analysis Techniques for Customer In-store Behavior in Supermarket

    Y. Zuo, K. Yada, T. Li, P. Chen

    2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)  2018.10 

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    Event date: 2018.10

    Venue:Miyazaki Japan  

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  • 視線追跡データを用いた消費者の店舗内購買行動の分析

    Y. Kaneko, K. Ishibashi, K. Yada

    2018.6 

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  • センサー、ビッグデータ、そしてマーケティング

    矢田勝俊

    IMI(マス・フォア・インダストリ研究所)  2018.2 

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    Event date: 2018.2

    Venue:福岡  

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  • データ分析とマーケティングモデルの発展

    K. Yada

    2017.12 

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    Event date: 2017.12

    Venue:コープさっぽろ, 北海道  

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  • A Framework of ASP for shopping path analysis

    K. Yada, K. Ichikawa, K. Takai, K. Miayazaki

    The 4th Asia-Pacific World Congress on Computer Science and Engineering 2017  2017.12 

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    Event date: 2017.12

    Venue:Mana Island Resort and Spa, Fiji  

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  • The Effect of Crowding on Visit Ratio at an Product Area: Based on RFID Data in a Japanese Supermarket

    B. Wu, K. Yada

    The 4th Asia-Pacific World Congress on Computer Science and Engineering 2017  2017.12 

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    Event date: 2017.12

    Venue:Mana Island Resort and Spa, Fiji  

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  • Do Sales Promotions Affect Dynamic Changes in Sales Outcomes: Estimation of Dynamic State of Product Sales

    Y. Kaneko, K. Yada

    The 4th Asia-Pacific World Congress on Computer Science and Engineering 2017  2017.12 

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    Event date: 2017.12

    Venue:Mana Island Resort and Spa, Fiji  

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  • Model selection for financial statement analysis: Comparison of models developed by using data mining technique

    K. Ishibashi, T. Iwasaki, S. Otomasa, K. Yada

    IEEE International Conference on System, Man, and Cybernetics  2017.10 

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    Event date: 2017.10

    Venue:Banff, Canada  

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  • データマイニングのビジネス応用における諸問題

    K. Yada

    2017.9 

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    Event date: 2017.9

    Venue:日本オペレーションズ・リサーチ学会, 大阪  

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  • 境界を越えるデータサイエンスとマーケティングモデル

    K. Yada

    2017.9 

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    Event date: 2017.9

    Venue:2017年度第3回オギノFSP研究会, 山梨  

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  • The Influence of Customer Movement between Sales Areas on Sales Amount: A Dynamic Bayesian Model of the In-store Customer Movement and Sales Relationship

    Y. Kaneko, S. Miyazaki, K. Yada

    21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems  2017.9 

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    Event date: 2017.9

    Venue:Marseille, France  

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  • Marketing and the Uses of Big Data” Asia Pacific for Computing and Information Technology

    K. Yada

    2017.7 

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    Event date: 2017.7

    Venue:APSCIT2017 Hokkaido Japan  

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  • A Framework of ASP for shopping path analysis

    Wai Tik So, K. Yada

    The 4th Multidisciplinary International Social Networks Conference  2017.7 

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    Event date: 2017.7

    Venue:Bangkok, Thailand  

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  • 流通業におけるデータサイエンスとのつきあい方

    K. Yada

    2017.5 

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    Event date: 2017.5

    Venue:日本流通産業㈱, 大阪  

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  • データ利活用の魅力と落とし穴

    K. Yada

    2017.3 

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    Event date: 2017.3

    Venue:ジャストシステムセミナー, 大阪  

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  • A Deep Learning Approach for the Prediction of Retail Store Sales

    Y. Kaneko, K. Yada

    2016 IEEE 16th International Conference on Data Mining Workshops  2016.12 

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    Event date: 2016.12

    Venue:World Trade Center Barcelona, Spain  

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  • Prediction of Consumer Purchasing in a Grocery Store Using Machine Learning Techniques

    Y. Zuo, K. Yada, A B M Shawkat Ali

    2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE 2016)  2016.12 

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    Event date: 2016.12

    Venue:Sofitel Fiji Resort & Spa, Denarau Island, Fiji  

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  • Vehicle Ownership and Economic Development

    Z. Li, K. Ishibashi, Y. Kaneko, K. Miayazaki, H. Shioji, K. Yada

    2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE 2016)  2016.12 

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    Event date: 2016.12

    Venue:Sofitel Fiji Resort & Spa, Denarau Island, Fiji  

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  • Does the Existence of Private-Label Brands Really Impede National Brands Sales? Empirical Evidence Based on POS Data

    Z. Li, K. Yada

    3rd International Conference of Asian Marketing Associations  2016.10 

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    Event date: 2016.10

    Venue:Beijing, China  

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  • Fractal Dimension of Shopping Path: Influence on Purchase Behavior in a Supermarket

    Y. Kaneko, K. Yada

    20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES2016  2016.9 

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    Event date: 2016.9

    Venue:York, United Kingdom  

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  • Data mining for marketing in the real world

    K. Yada

    2016.8 

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    Event date: 2016.8

    Venue:MISNC 2016, New Jersey USA  

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  • Impact of Analog-to-digital Conversion on Predictive Performance: A Case Study of Bayesian Network vs. Support Vector Machine in Purchase Behavior Prediction

    Y. Zuo, K. Yada, E. Kita

    2016 World Congress on Computational Mechanics, Seoul Korea  2016.7 

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    Event date: 2016.7

    Venue:Seoul, Korea  

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  • データマイニングの応用

    K. Yada

    2016.6 

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    Event date: 2016.6

    Venue:第20回日本医療情報学会春季学術大会,島根県松江市  

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  • Complementary Relationship between Private Brands and National Brands: Empirical Evidence Based on POS Data

    Z. Li, K. Yada

    38th ISMS Marketing Science Conference  2016.6 

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    Event date: 2016.6

    Venue:Shanghai, China  

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  • Self-control and consumer behavior

    S. Shibasaki, K. Takai, K. Yada

    International Marketing Trends Conference  2016.1 

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    Event date: 2016.1

    Venue:Venice, Italy  

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  • Human Behavior and Marketing

    K. Yada

    IEEE Asia-Pacific World Congress on Computer Science and Engineering 2015  2015.12 

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    Event date: 2015.12

    Venue:Fiji  

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  • Why Do Retailers End Price Promotions: A Study on Duration and Profit Effects of Promotion

    Z. Li, K. Yada

    2015 IEEE International Workshop on Data Mining for Service  2015.11 

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    Event date: 2015.11

    Venue:Atlantic City, NJ, USA  

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  • ビジネスにおけるデータ活用の現状と課題

    矢田勝俊

    オペレーションズリサーチ学会シンポジウム  2015.9 

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    Event date: 2015.9

    Venue:福岡  

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  • ビジネスにおけるセンサーデータとデータサイエンスの最前線

    矢田勝俊

    第32回スーパーコンピューティング・セミナー  2015.9 

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    Event date: 2015.9

    Venue:東京  

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  • Verification of effect on next purchase when many vice category products are brought

    K. Ishibashi, K. Miayazaki, K. Yada

    19th International Conference on Knowledge-Based and Intellegent Information & Engineering Systems - KES 2015  2015.9 

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    Event date: 2015.9

    Venue:Marina Bay Sands Hotel, Singapore  

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  • Visualization System for Shopping Path

    Y. Kaneko, K. Yada

    19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems - KES 2015  2015.9 

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    Event date: 2015.9

    Venue:Marina Bay SandsHotel, Singapore  

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  • バースト検知手法を用いたレジの混雑状況の特定

    矢田勝俊

    日本オペレーションズ・リサーチ学会2015年春季研究発表会  2015.3 

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    Event date: 2015.3

    Venue:東京  

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  • How Does Purchase of a Product Affect the Next Purchase?

    K. Yada

    14th International Marketing Trends Program Conference  2015.1 

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    Event date: 2015.1

    Venue:Paris, France  

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  • Big Data and Marketing

    K. Yada

    IEEE APWC on CSE 2014  2014.11 

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    Event date: 2014.11

    Venue:Nadi, Fiji  

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  • ビジネスにおけるビッグデータの利活用 -流通小売業の現場から-

    矢田勝俊

    CREST戦略的創造研究推進事業  2014.11 

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    Event date: 2014.11

    Venue:東京  

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  • Using Bayesian Network for Purchase Behavior Prediction from RFID Data

    Y. Zuo, K. Yada

    The 2014 IEEE International Conference on Systems, Man, and Cybernetics  2014.10 

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    Event date: 2014.10

    Venue:San Diego, CA, USA  

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  • Consumer Purchasing Behavior Extraction Using Statistical Learning Theory

    Y. Zuo, A B M Shawkat Ali, K. Yada

    18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems  2014.9 

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    Event date: 2014.9

    Venue:Gdynia, Poland  

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  • Category Evaluation Method for Business Intelligence Using a Hierarchical Bayes Model

    N. Sano, K. Yada, T. Suzuki

    13th IEEE International Conference on Cognitive Informatics & Cognitive Computing  2014.8 

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    Event date: 2014.8

    Venue:Singapore  

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  • 購買行動研究の最前線

    矢田勝俊

    オギノFSP研究会  2014.5 

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    Event date: 2014.5

    Venue:山梨  

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  • Determining the Share of Product Categories on Discount Flyers Based on the Interaction Effect between Bargain Scale and Sales Area

    N. Sano, K. Yada

    Proceedings of 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing  2013.7 

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    Event date: 2013.7

    Venue:New York, USA  

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  • Clockwise and Anti-clockwise Direction of Customer Orientation in a Supermarket: Evidence from RFID Data

    M. Kholod, K. Takai, K. Yada

    Proc. of KES 2011, Knowledge-Based and Intelligent Information and Engineering Systems, Lecture Notes In Artificial Intelligence(LNAI) 6883  2011.9 

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    Event date: 2011.9

    Venue:Kaiserslautern, Germany  

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  • A Framework of Shopping Path Research

    YADA,Katsutoshi

    2011.1 

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    Event date: 2011.1

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  • Relation between stay-time and purchase probability with RFID data in a Japanese supermarket

    Keiji Takai, Katsutoshi Yada

    Proceedings of KES 2010, Knowledge-Based and Intelligent Information and Engineering Systems, Lecture Notes In Artificial Intelligence (LNAI) 6278  2010 

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    Event date: 2010

    Venue:Cardiff, UK  

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  • Data Mining Application for Time-Series Data

    YADA Katsutoshi

    Joint Conference on Information Sciences 2007  2007.7 

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    Event date: 2007.7

    Keynote Speech

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  • Business Application and Risk of Data Mining

    YADA Katsutoshi

    International Workshop on Risk Informatics (RI2007)  2007.6 

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    Event date: 2007.6

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  • Data Mining Technique for Gene Analysis Makes Profits in the Supermarket

    YADA Katsutoshi

    2007 AMA Winter Educators’ Conference  2007.2 

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    Event date: 2007.2

    Venue:San Diego  

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  • The River-Rafting System for Knowledge Discovery Related to Persuasion Process Conversation Logs

    YADA Katsutoshi

    IEEE International Workshop on Data Mining for Design and Marketing, in conjunction with 6th IEEE International Conference on Data Mining  2006.12 

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    Event date: 2006.12

    Venue:Hong Kong  

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  • Knowledge Discovery from the Structure of Persuasive Communication

    YADA Katsutoshi

    2006 IEEE Conference on System, Man and Cybernetics  2006.11 

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    Event date: 2006.11

    Venue:Taipei  

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  • Knowledge Discovery from Click Stream Data and Effective Site Management

    YADA Katsutoshi

    International Workshop on Risk Mining 2006  2006.6 

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    Event date: 2006.6

    Venue:Tokyo  

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  • Analysis on a Relation between Enterprise Profit and Financial Sate by Using Data Mining Techniques

    YADA Katsutoshi

    International Workshop on Risk Mining 2006  2006.6 

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    Event date: 2006.6

    Venue:Tokyo  

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  • What Effect does a Crisis Have on Consumer Behavior?: The Example of a food Poisoning Incident in Japan

    YADA Katsutoshi, T.Arakin, D.Fujishima

    European Applied Business Research Conference 2004  2004.6 

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  • Crisis and Consumer Behavior

    FUJISHIMA Daisuke, ARAKI Takaharu, YADA Katsutoshi

    The 51st Artificial Intelligence Foundation Study Meeting  2003.1 

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    Event date: 2003.1

    In this paper we present the implication of risk management to analyze the situation of crisis in detail based on consumer behavior. We discuss about the case of milk poisoning in Japan to grasp the individual consumer behavior by making use of FSP data to discover new knowledge.

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  • Chance Discovery from Image of Consumer using Key Graph

    NIWASE Seiko, HAMADA Saori, ARAKI Takaharu, MATSUMURA Naohiro, OSAWA Takao, YADA Katsutoshi

    The 51st Aritificial Intelligence Foundation Study Meeting  2003.1 

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    Event date: 2003.1

    We present a methology to understand consumer behavior by constructing the research process so as to integrate qualitative and quantitative analysis. This paper briefly describes text mining process using KeyGraph for extracting key factors of drug store image.

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  • Customer Attrition Analysis in Supermarket

    YADA Katsutoshi

    International Business & Economic Research Conference 2003 (IBER 2003)  2003 

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    Event date: 2003

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  • データマイニングプロジェクトのマネジメント

    矢田 勝俊

    人工知能学会第18回AIシンポジウム  2003 

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    Event date: 2003

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  • 「各種入試合格者への入学前教育指導改善による英語力向上」成果報告書

    矢田 勝俊

    2021.8 

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  • Sensor Marketing and Data Mining Invited

    YADA,Katsutoshi

    KES 2019  2019.9 

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    Language:English   Presentation type:Oral presentation (keynote)  

    Venue:Budapest, Hungary  

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  • Data Mining Application for Time-Series Data

    YADA Katsutoshi

    Joint Conference on Information Sciences 2007  2007 

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Awards

  • The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology, Prizes for Science and Technology.

    2019.4   Minister of Education, Culture, Sports, Science and Technology   The promotion of data mining technology to analyze big data business marketing

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    Country:Japan

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  • The best paper for Japan Award

    2016   ICAMA 2016  

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  • IEEE SMC 2014 The best conference paper award, finalist

    2014   2014 IEEE Conference on System, Man and Cybernetics  

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  • 人工知能学会研究会優秀賞

    2011   人工知能学会  

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    Country:Japan

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  • マーケティング分析コンテスト 2010最優秀賞

    2011.1   野村総合研究所マーケティング分析コンテスト事務局  

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    Country:Japan

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  • マーケティング分析コンテスト 2010 最優秀賞

    2011.1   野村総合研究所マーケティング分析コンテスト事務局  

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    Country:Japan

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  • The Session Best Paper Award at SCIS & ISIS 2006

    2006.9   SCIS & ISIS 2006  

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    Country:Japan

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  • 著述賞

    2005.8   知能情報ファジィ学会  

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    Country:Japan

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  • 2003 International Development Award

    2003.8   Center for International Business Education and Research (CIBEAR), Marshall Business School  

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  • 人工知能学会研究会優秀賞

    2003.6   人工知能学会  

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    Country:Japan

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  • SPSS Open House研究奨励賞SPSS賞

    2002.10   SPSS Open House 事務局  

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    Country:Japan

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  • 平成七年度兼松フェローシップ大学院生研究奨励賞

    1995.8   神戸大学経済経営研究所  

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    Country:Japan

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Research Projects

  • A Study of Cognitive Processes in Investor Decision Making Using Eye Tracking

    Grant number:22H00888  2022.4 - 2026.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Grant amount:\16640000 ( Direct Cost: \12800000 、 Indirect Cost:\3840000 )

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  • A study of the model of COVID-19 infection spread from the perspective of consumer behavior

    Grant number:21K18446  2021.7 - 2024.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Challenging Research (Exploratory)

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    Grant amount:\6240000 ( Direct Cost: \4800000 、 Indirect Cost:\1440000 )

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  • パスデータの融合による研究フロンティアの創出

    Grant number:16H02034  2016.4 - 2021.3

    日本学術振興会  科学研究費助成事業  基盤研究(A)

    矢田 勝俊, 高井 啓二, 宮崎 慧, 石橋 健, 李 振, 里村 卓也, 金子 雄太, 中原 孝信, 左 毅, 市川 昊平

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    Grant amount:\38090000 ( Direct Cost: \29300000 、 Indirect Cost:\8790000 )

    多様なパスデータを用いた解析、モデル化を行うために、(1)消費者行動モデルの構築、(2)国際ワークショップの開催、(3)パスデータ融合の体系化に取り組み2019年度の計画通り研究を遂行することができた。
    (1)2018年度に実施した店舗実験で収集したパスデータをもとに、様々な消費者行動モデルを構築した。セルフコントロールが店内購買行為に与える時系列の影響についてモデル化に成功、また視線追跡を統合し、個人の異質性を前提とした確率モデルを提示することができた。
    (2)国際ワークショップの開催では、KES2019に併設する形で招待セッションを主催し、5本の発表を受入ながら、ブダペスト(ハンガリー)で行われた。顧客動線、視線追跡などの発表も含め、重要な意見の交換が行われた。そしてIEEEが主催する国際会議ICDMに併設する形で、Data mining for serviceという国際ワークショップを主催し、多くの発表を受入ながら、本提案の発表を行った。また、天野教授(ハーバード大学)やWedel教授(メリーランド大学)と議論しながら、体系化について貴重なアイデアを得ることができた。特にバースト検知手法を用いた混雑状況予測モデルについて、体系化が進み、論文化のめどをつけることができた。
    (3)体系化については、上述したような海外の研究者との議論を通して体系化のアイデアをまとめつつ、本提案チームの中で議論を深めることができた。ただし、3月に実施予定であった今期、最後の打合せが新型コロナウイルス感染症の感染拡大をうけて中止になった。

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  • Dynamic Modeling of Consumer Purchase Behavior: Based on Web Crawling Data

    Grant number:15H06747  2015.8 - 2017.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Research Activity Start-up

    Li Zhen, ZHANG Jie, MA Yan, HUANG Lin, YADA Katsutoshi

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    Grant amount:\2340000 ( Direct Cost: \1800000 、 Indirect Cost:\540000 )

    There is much evidence that customer reviews are important to merchants' sales. However, little is deeply discussed about how much the online reviews affect sales, and the validity of the influence also lacks supports of empirical studies. This study posits that sales effects of customer reviews are different by market structure.
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    We propose a Bayesian statistical model to study how customer reviews affect sales and the difference in its influence between two types of merchants, market-place and self-conducted stores. The data in this study are retrieved by using Web Crawling technology. The findings suggest that (1) sales effects of review volume will be attenuates by the information of sales outcomes in previous period, (2) compared with self-conducted stores, the volume of customer reviews seem more effective on sales for merchants in market-place, and (3) the effects of review valence on sales are also somewhat stronger for market-place stores than it for self-conducted stores.

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  • Experiment and Modeling of Consumer Behavior Analysis by Using Shopping Path Data

    Grant number:22243033  2010.4 - 2015.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    YADA Katsutoshi, KAWAKAMI Tomoko, NAKAHARA Takanobu, ICHIKAWA Kouhei, NISHIOKA Kenichi, TAKAI Keiji

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    Grant amount:\32500000 ( Direct Cost: \25000000 、 Indirect Cost:\7500000 )

    The purpose of this research is to provide a model of consumer behavior which presents the relationship between consumer's in-store movement and purchasing behavior. We integrate customer's purchase history data with shopping path data which is consumer's in-store movement data in a retailer and then present various theoretical models such as customer's behavior model based on a string analysis, a time-series model of customer's movement and so on. Actually we implemented some marketing promotions in the real retailer based on our findings and achieved good sales performance. In conclusion we present a comprehensive framework of shopping path research based on these findings. We show significant implications for retail business and some theoretical contributions in marketing.

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  • Analysis Framework for Persuading Process and an Application to Debt-Collecting Conversation Logs

    Grant number:19730275  2007 - 2010

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (B)

    YADA Katsutoshi

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    Grant amount:\2910000 ( Direct Cost: \2400000 、 Indirect Cost:\510000 )

    The purpose of this research is to develop a framework and an analysis method to discover some features from the content and the persuading process in un-structured databases, and to show its application to the communication for the debt-collecting process to verify the usefulness of the proposed system. In this study, we were successful in clarifying the communication process in the overdue payment collection by using the data of a Japanese telecommunication company.

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  • 大規模次元観測時系列からのダイナミクス知識体系化と理解支援手法の開発

    Grant number:19024048  2007 - 2008

    日本学術振興会  科学研究費助成事業  特定領域研究

    鷲尾 隆, 矢田 勝俊, 大原 剛三, 猪口 明博

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    Grant amount:\6100000 ( Direct Cost: \6100000 )

    本研究では, 大規模次元で表わされる状態が時間的に変化する対象系を観測したデータから, 対象の状態遷移規則を表す知識体系とそれを理解支援する技術を確立し, ICチップなどによる商業用物流・人間移動のユビキタス追跡分析・監視システムを実現する基礎原理を得ることを目的とした.
    今年度は, 前年度の手法適用を通じて問題が明らかになった,(1)個々の状態遷移規則同士の因果関係が従うべき数理的, 確率的, 物理的制約を用い, 対象の有意味な状態遷移に関する知識体系を同定する技術の開発, 及び(2)そこから特定部分状態関係を含む状態遷移規則やその規則同士の特徴的関係を把握する技術の問題点を克服する改良, 拡張に取り組んだ. 前者に関しては対象システムが取る可能性のある多くの状態候補を計算し, それら状態を確率的に統合して対象の状態とその状態遷移を推定する原理が, 特に大規模次元状態空間内で高精度, 高効率に動作する技術を開発した. 後者については, 更に特に実状態である可能性の高い状態を導く特徴的な遷移を把握し, 結果の理解容易性と同時で状態推定精度を高める方法を開発した.
    以上のために, 大阪大学の研究代表者(鷲尾)と関西大学の連携研究者(矢田)間の定期的検討会を持って緊密に連携し, 更に改良・拡張した手法を実データに適用して, 大規模変数次元時系列観測データのダイナミクスに関して総合的な知識体系を得, そのユーザー理解支援を十分に実現可能な技術改良, 拡張を行った. また, この研究過程において, 2名の大阪大学産業科学研究所の研究者(大原, 猪口)から, 主にデータ処理や実験検証の面で連携研究者として協力を得た.

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  • Scenario Map Systems for Aiding Organizational Chance Discovery

    Grant number:16200006  2004 - 2007

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    OHSAWA Yukio, YAMADA Yuji, YADA Katsutoshi, TAKAMA Yasufumi, SUNAYAMA Wataru

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    Grant amount:\31590000 ( Direct Cost: \24300000 、 Indirect Cost:\7290000 )

    In order to aid in the organizational decision process, we invented methods for constructing systems for visualizing scenario maps from the real data on business and having people do collaborations by sharing the obtained image. Here, an organization means a number of people who share the same purpose, exemplified by (a) the members of the present project, (b) a laboratory in a university, and c a collaboration team of members coming from across difference companies. The fundamental functions aimed at by our study were: (1) scenario maps visualizing data on business, (2) the human-machine interface to enable collaborating members to create future scenarios on the scenario map, and (3) aid the collection of suitable members for the collaboration from remote working places.
    From the project of four years, we realized not only the remote meetings of people from different institutes, but also showed real-space meetings of known users are more important for real business decisions. The method we developed enables creative communications of both on-line (remote) and on-site (in the same room) members. Although we changed the research sub-goal (3) above, i.e., to search suitable members, into to put fixed members in a well-designed environment, the results show stronger creativity of collaborators' organization.
    In order to realize this application-oriented fruit, we also founded basic components, i.e., the algorithms for data analysis. The mathematical model by Yamada and Sunayama provides useful information to users in business, and the interface modules presented by Takama enables a number of collaboration members to achieve high performance of a project. The overall result of this research project has been presented to business users as well as academic researchers, with the frequent interaction with user's real working places by Ohsawa and Yada.

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  • 情報技術導入におけるビジネスシステム構築プロセスの理論と実践

    Grant number:16016282  2004 - 2005

    日本学術振興会  科学研究費助成事業  特定領域研究

    矢田 勝俊, 加藤 直樹, 羽室 行信, 岸谷 和広

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    Grant amount:\8200000 ( Direct Cost: \8200000 )

    情報機器の低価格化、インターネットの急激な普及によって、あらゆるところに莫大なデータが蓄積され、多くの日本企業がこうしたデータに注目し、価値ある知識を発見しようと様々な試みが行われている。しかし、現実には莫大な投資が行われているにも関わらず、十分な成果が得られているとは限らない。大規模なデータベースから有用な知識を発見する「データマイニング」という技術をどのようにビジネスプロセスに統合し、新しいビジネスシステムを構築するのか、理論的・実践的な研究が切望されている。
    本研究では、大規模なデータベースから有用な知識を発見する「データマイニング」技術を現実の企業へ導入し、実践科学的にその効果を検証する。我々が最も重要だと考えるのは、そうした新しい情報技術の導入に際して、新しいビジネスシステム、組織間関係の構築プロセスを理論化することである。新しい技術は新しいビジネスシステムや組織間関係のもとで、新しい価値をもたらすとすれば、そうしたプロセスを理論化し、より効率的な方法を模索することが社会的にも大きな意義をもつものと考えられる。
    我々は3つのパートでそれぞれ、重要な成果を達成することができた。プラットフォーム開発ではオープンソース・ソフトウェアとしてMUSASHIを開発し、多くの企業の導入実績をあげることができた。そしてビジネス・アプリケーションの開発としても、E-BONSAIやC-MUSASHI、PRISMなどを開発し、経営現場での実践を通して有用性を検証できた。そしてこうしたプラットフォームやアプリケーションをビジネスプロセスに導入するためのプロセスモデルの理論化も行い「データマイニングと組織能力(多賀出版)」としてまとめることができた。これらの成果は大学研究者と企業の実務家が情報・意見交換を行うビジネスマイニングワークショップで発表され、社会への還元の仕組みが完成している。

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  • データマイニングにおけるITと組織の統合ビジネスプロセス理論の構築

    Grant number:15730198  2003 - 2005

    日本学術振興会  科学研究費助成事業  若手研究(B)

    矢田 勝俊

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    Grant amount:\3500000 ( Direct Cost: \3500000 )

    近年、インターネットに代表されるITが企業内外に導入され、既存のビジネスシステムは変化を迫られている。企業は新しい技術環境に適応し、新しいビジネスシステムの構築に積極的に取り組み、自社の競争優位を維持していかなければならない。しかしながら、そのようなビジネスシステムの構築が効率的に行われているとはいえないケースが多々見られる。そうした先端技術の代表的なものとしてデータマイニングがあげられる。データマイニングは大規模なデータベースから有用な知識を発見するためのシステム、プロセスをさす。
    本研究の目的は、データマイニングのビジネスヘの導入プロセスを組織論、戦略論的観点から考察を行い、ビジネスプロセス構築の理論化を行うことである。本研究の特徴は実践を通して、構築した理論の検証を行う点にある。従来の社会科学の研究では、過去の事実を理論化する研究はあっても、事実に積極的・直接的に関与し、それらを理論化する研究はほとんどない。我々は理論と実践の融合を目指し、新しい研究体制の下で研究を遂行した。
    我々はビジネスプロセスの構築に実際に関与し、ドメイン知識(専門家の特化した知識)を導入するプロセスを理論化した。また、顧客の販売履歴からの知識発見という領域に限定することで、新しい知識創造プロセスのモデル化を行い、海外の書籍に採用された。

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  • Development of knowledge discovery system and research of business process for the implementation

    Grant number:15500096  2003 - 2004

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    HAMURO Yukinobu, KATOH Naoki, YADA Katsutoshi

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    Grant amount:\3700000 ( Direct Cost: \3700000 )

    In our research project, we have tried to develop the KDD system that can be applied to the entire KDD process, for the purpose that the system will be used in an actual company. We have achieved the following three points.
    1.Analytical methods
    We had joint research projects with some companies (retail companies like supermarket and department store) on knowledge discovery. Those companies provided us with actual sales transaction data with customer ID. Using those data we developed analytical methods that provide a useful knowledge, by which the companies can implement an effective marketing action. We developed following three methods mainly.
    (1)Knowledge discovery on store arrangement in a department store using genetic algorithm
    (2)Knowledge discovery in brand purchasing pattern using network flow estimation
    (3)Consumer Behavior Analysis by graph mining technique
    2.Algorithms used in the methods
    We developed efficient algorithms for the above analytical methods. We developed the following three methods mainly.
    (1)Algorithms required for data preprocessing
    (2)Extension of the algorithm originally used in gene analysis technology to the analysis of business data
    (3)Efficient algorithms for approximating a multi-dimensional voxel terrain by a unimodal terrain
    3.Developing a software
    We have developed a KDD software named "MUSASHI", which was released on the web of "musashi.sourceforge.jp" on 10 July, 2003. Most of the analytical methods and the algorithms mentioned above were already implemented in MUSASHI.
    More than 60,000 of people had visited on the web page and the software had been downloaded about 5,200 times since opening the web page, which shows our research result is generally accepted.

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  • 情報技術導入におけるビジネスシステム構築プロセスの理論化

    Grant number:15017282  2003

    日本学術振興会  科学研究費助成事業  特定領域研究

    矢田 勝俊, 加藤 直樹

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    Grant amount:\2500000 ( Direct Cost: \2500000 )

    本研究は,基礎技術の開発,システムアーキテクチャ,データからの知識発見,それに基づいたビジネスアプリケーションの開発,ビジネスシステムの構築,そしてその実践による効果検証という,情報技術導入におけるビジネスシステム構築プロセスの包括的理論を構築することを目的としている.
    本年度までに,我々はこれらの理論面の検討を行うことができた.その内容は相互に関連する3つの領域に分けることができる.第1にビジネスで利用されるアプリケーションの開発である.本年度はMUSASHIをベースにしたCRMシステムを開発し,企業データの解析実験を行った.第2はそれらを支える基礎技術の開発であり,オープンソースプラットフォームMUSASHIを公開し,多くの企業が参加する日本MUSASHIユーザー会を中心に普及を進めている.第3の領域はビジネス応用のためのビジネスシステム構築プロセスの理論化の問題である.経営資源論に依拠し,組織能力の概念からこれらのプロセスの理論化を行った。
    本年度はこれらの理論部分の検討を研究書としてまとめることができ,『データマイニングと組織能力』として出版することができた.今後,これらの理論モデルに基づいたシステム導入を進め,実践による検証を行う予定である.

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  • 情報技術導入におけるビジネスシステム構築プロセスの理論化

    Grant number:14019083  2002

    日本学術振興会  科学研究費助成事業  特定領域研究

    矢田 勝俊, 加藤 直樹

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    Grant amount:\3000000 ( Direct Cost: \3000000 )

    本研究では、従来、研究レベルで開発してきたマーケティング用のシステムを、現実で利用可能なパッケージへと改良して、それを現実の企業へ導入し、実践科学的にその効果を検証する。我々が最も重要だと考えるのは、そうした新しい情報技術の導入に際して、新しいビジネスシステム、組織間関係の構築プロセスを理論化することである。新しい技術が新しいビジネスシステムや組織間関係のもとで、新しい価値をもたらすとすれば、そうしたプロセスを理論化し、より効率的な方法を模索することが社会的にも大きな意義をもつものと考えられる。
    本年度、大別して2つの領域において上記の研究を進展させることができた。第一に、データマイニング技術を利用したマーケティングシステムとして、時系列解析をベースにした需要予測システムを開発した。新しい情報技術として今後、これらをビジネスのフィールドに導入していく予定である。
    第二にデータマイニングを利用したビジネスプロセスの理論化を行った。大規模データから有用な知識を発見するプロセスを理論化し、さらにそのプロセスの効率化に関するマネジメント戦略を提起した。
    本年度までで、新しい情報技術の導入、それを利用したビジネスプロセス、知識発見プロセスの理論化までを行い、知識発見プロセスの組織戦略まで明らかにすることができた。今後、これらの研究をベースにして、利害関係者すべてを含むビジネスシステム(ビジネス全体の仕組み)の構築プロセス、組織能力の構築理論を明らかにしていきたい。

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  • 構造データからのアクティブマイニング

    Grant number:13131206  2001 - 2004

    日本学術振興会  科学研究費助成事業  特定領域研究

    元田 浩, 鷲尾 隆, 大原 剛三, TUBAO Ho, 矢田 勝俊, 吉田 哲也

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    Grant amount:\64800000 ( Direct Cost: \64800000 )

    研究実績は以下のとおり.
    1.グラフ構造データからの決定木構築プログラムDT-GBIでの探索過程に領域知識の制約(指定したパターンを含む,含まない)を導入した.
    2.ペアを仮想ノードとして扱い,チャンキングをせず探索する新グラフマイニング手法Cl-GBIを開発した.適切なパラメータ設定により完全探索が可能になり,GBIの数え落としの問題点などを解決した.
    3.上記Cl-GBIを組み込んだ決定木構築プログラムDT-ClGBIを開発し,肝炎データセットで性能を評価した.
    4.数値データを伴うデータから,数値を記号離散化することなしに相関の高い数値区間を自動抽出する原理を確立し,それに基づく数値相関規則導出手法を開発した.
    5.ユーザ指向データマイニングシステムD2MSの肝炎患者に関するルールの理解容易性向上を確認し,多数のルールから統計的に有意なものを選定する手法とルール学習において領域知識を表現の制約に加える手法を提案した,
    6.科学データマイニングとしてゲノムおよび結晶データを並行して解析した.前者に関しては,SVMによるタンパク質の2次構造におけるβターンの予測手法を拡張しγターンを予測した.後者に関しては,粉末回折データから結晶構造を同定する手法を遺伝的アルゴリズムに基づき開発した.
    7.意味的まとまりを捉えたパッセージの集合として文書を表し,トレランス・ラフ集合モデルによるソフトマッチを導入し,意味を反映した相関ルールを得る手法を開発した.
    8.グラフマイニング手法AGMを消費者行動データに適用し,アクティブマイニングによる実証実験を行い新しいデータを収集した.アルコール市場分析から得られた知見に基づき,実際の店舗で店頭プロモーションを行った結果,対象商品の売り上げ増加,関連商品の同時購入頻度の増加を検証することができた.

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  • 情報技術導入におけるビジネスシステム構築プロセスの理論化

    Grant number:13224085  2001

    日本学術振興会  科学研究費助成事業  特定領域研究(C)

    矢田 勝俊, 加藤 直樹

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    本研究の目的は、新しい情報技術の導入によって再構築されるビジネスシステムのプロセスを理論化し、日本企業の情報インフラの効果的な設計、生産性の向上に貢献することである。既存研究の多くは、企業の情報化投資と生産性には直接的な因果関係がないことを指摘している。競争環境の激化は情報技術の更なる有効利用を企業に突きつけているが、多くの企業が新しい情報技術に対応した新しいビジネスシステムを構築できずにいる。その原因は日本企業の文化にあったシステムアーキテクチャの不在とその導入プロセスが不明瞭な点である。
    本研究では、我々が開発したシステムアーキテクチャを企業に導入し、新しいビジネスシステムを構築していくプロセスを理論化する。情報技術の特性と組織との関係、競争戦略における情報技術の位置付けなどを明らかにし、情報技術をより効果的に生産性に結びつける方法論を提示している。
    我々は消費者行動に関する分析システムに研究の焦点を当てている。現在、日本の流通システムでは多くのデータや情報が孤立しており、十分な消費者行動に関する分析ができない状態である。また近年のデータ蓄積は巨大化する一方で、通常の(基幹系の)システム技術では対応は難しい。そこで、大規模データベース解析に特化した我々のシステムアーキテクチャを導入し、それぞれのデータを結びつけることで、ビジネスチャンスを発見しようと試みている。
    本研究の特色の第一は、技術開発のみならず、その実践、効果検証、理論化という社会科学的アプローチを同時に行い、情報技術の影響を総合的に捉える文理融合の研究であること。第二に、研究成果が実践され、その有用性が証明されること。第三に、日本企業の情報インフラに、大学の最先端の研究蓄積を包含した1つの選択肢を提供することによって、社会へ貢献しようとする点をあげることができる。

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  • データマイニング技術の適用問題と動学的戦略論の枠組みの提示

    Grant number:11730070  1999 - 2000

    日本学術振興会  科学研究費助成事業  奨励研究(A)

    矢田 勝俊

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    Grant amount:\2100000 ( Direct Cost: \2100000 )

    本研究では、データマイニング技術の経営領域への適用問題に取り組み、またそのための戦略的枠組みを提示した。まず、データマイニング技術の適用問題に関しては、様々な実験を行い、その有用性を明らかにした。
    特にロイヤルカスタマー(優良顧客)の早期発見に関する研究で、新規顧客から早期に優良顧客を識別する問題に取り組み、2ヶ月間のデータで、1年後の優良顧客を85%の確率で予測できるモデルを構築した。これは従来の方法よりも、20%以上、予測確率を向上させることに成功しており、現実の世界での有用性を示すことができた。
    また、戦略枠組みにおいては、コミットメント論を中心に動学的戦略を研究し、データマイニング適用のための基礎的枠組みを構築した。基本的枠組みから得られる示唆は、情報システムを構築する際の設計思想、システムアーキテクチャが非常に重要であり、変化の激しい環境においては、その柔軟性が成功に大きな影響を与えることを明らかにした。具体的な設計思想として、履歴ベースという設計思想概念を提唱し、実装技術の開発を行った。その導入企業では、大幅なコスト削減が可能になった。また、システム開発期間の短縮、ユーザビリティの向上など、様々なメリットが認識された。
    今後は、こうしたシステムアーキテクチャを様々な業界で導入し、どのぐらいの効果があるかを実験、検証していかなければならないと考えている。そして、従来の設計思想との比較を行い、新しい理論構築をする必要があろう。

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  • Knowledge Discovery in Databases

    Grant number:10143102  1998 - 2000

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research on Priority Areas (A)

    MIYANO Satoru, YAMAGUCHI Takahira, MOTODA Hiroshi, KITAGAWA Genshiro, SUZUKI Einoshin, MORISHITA Shinichi

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    Grant amount:\119800000 ( Direct Cost: \119800000 )

    Scientists are struggling for creating better data, observations, and measurements in various fields. Their ultimate target is a discovery from such data. The process starting from data creation and ending with a series of discoveries is the object of our research. Thus the aim of our research is to create computational strategy for speeding up the discovery process in total. For this purpose, this project is organized with researchers working in scientific domains and researchers from computer science so that real issues in the discovery process will be exposed out and practical computational techniques will be devised and tested for solving these real issues.
    Group A04 has succeeded in creating solutions to this problem and a bunch of novel methods with which the discovery process can be sped up efficiently. The contributions of this project can be summarized in three items :
    1. Development of new methods/models for efficient/feasible processing in stages in computational discovery. All methods are developed for practice but most of them are well-abstracted enough for general purpose application.
    2. Integration strategy for the total discovery process and paradigm of discovery system.
    3. Discoveries
    In addition to these individual research activities, this project took the initiatives in enlightenment of Discovery Science which are published as "S. Miyano (Guest Chief Editor), Surveys on Discovery Science, Special Issue, IEICE Transactions on Information and Systems, Vol. E83-D, No, 1, 2000" and "S. Morishita and S. Miyano (eds), Discovery Science and Data Mining, Kyoritsu Shuppan, 2000."

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  • 経営データにおけるデータマイニング基礎技術の開発、ならびに戦略的活用に関する研究

    Grant number:10143107  1998

    日本学術振興会  科学研究費助成事業  特定領域研究(A)

    矢田 勝俊, 羽室 行信, 加藤 直樹

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    Grant amount:\2800000 ( Direct Cost: \2800000 )

    本研究では、ビジネス分野におけるデータマイニング基礎技術、ならびに実装技術の開発を発見科学の立場から行うものである。
    データマイニング技術を適用するには、まだ多くの基礎技術の開発が欠かせない。本研究では、クラス間分散最大区間を求めるアルゴリズムと二次元への拡張を行った。求める領域を矩形領域に限定することによって、クラス間分散を最大化するような領域を求める高速アルゴリズムを開発した。さらに、1次元配列だけではなく、2次元配列における問題に拡張できるようにした。また、実装技術としては、履歴ベースの考え方を提示し、データマイニング志向のデータ蓄積技術を開発した。従来の企業で採用されている蓄積方法はデータマイニングプロセスで必要となる重要なデータが消失してしまうが、我々の提示する履歴ベースは、時系列データを蓄積することによって、データマイニングプロセスをサポートすることが可能になっている。これらは既に、企業への導入が行われているものであるが、その蓄積されたデータをもとに、実証実験を行い、現実への有用性を我々は明らかにすることができた。特に、企業の販売促進戦略への応用は将来的に、十分な効果が期待できるものと思われる。ダイレクトメールのターゲット顧客の選定にアソシエーションルールのアルゴリズムを適用し、検証の結果、通常の4倍の効率向上を確認することができた。
    我々は、こうした経営データに対してデータマイニング技術を適用し、基礎技術、実装技術の開発、その実証を通して、発見科学の経営領域の研究に寄与できるものと考えている。

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Devising educational methods

  • DSIプログラムを開発し、文部科学省の産学連携による実践型人材育成事業に採択され、実践力の高い学生の教育を実施している。本教育プログラムでは、巨大データを自由に分析し、その中から有用な知識を発見し、新しい価値を創出する様々なスキルを学習させている。産学連携ではFSP研究会、産学連携ワークショップを開催し、国内100社以上の企業の参加を得て、学生の教育成果の発信を行っている。

Teaching materials

  • DSIプログラム関連教育科目の教材一式 教科書としては、 加藤直樹・羽室行信・矢田勝俊『データマイニングとその応用』朝倉書店, 2008. などがある。

Teaching method presentations

  • K. Ichikawa, K.Yada, N. Nakachi and T. Washio, “Advertising Carryover Effects and Optimal Budget Allocation”, Proc. of KES 2009, LNAI 5712, pp.270-277, 2009. M. Kholod, T. Nakahara, H. Azuma, and K. Yada. ''The Influence of Shopping Path Length on Purchase Behavior in Grocery Store'', Proceedings of KES 2010, Knowledge-Based and Intelligent Information and Engineering Systems, Lecture Notes In Artificial Intelligence(LNAI)6278, pp. 273-280, September,2010.

Special notes on other educational activities

  • マーケティング分析コンテスト 2010 最優秀賞, 金東賢, 中原孝信, 矢田勝俊, 投稿論文名「イノベータ理論に基づくスマートフォン市場成長期の顧客特徴抽出と販売戦略の提案 -クリークを用いたXperiaの購入プロセスの解明 -」, 株式会社野村総合研究所マーケティング分析コンテスト事務局主催, 2010.(2011/1/)