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

DOI： 10.1109/spac49953.2019.237870

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

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

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

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

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

DOI： 10.1109/smc.2018.00322

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

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

CiNii Books

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

CiNii Books

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

CiNii Books

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

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

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

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.

DOI： 10.1007/s10844-012-0223-6

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

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

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

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

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

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

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.

DOI： 10.3233/978-1-61499-105-2-2104

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

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.

DOI： 10.1007/s10844-009-0113-8

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

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

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

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.

DOI： 10.1007/978-3-642-22732-5_27

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

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

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.

DOI： 10.1109/ICDMW.2011.30

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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

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.

DOI： 10.1504/IJAIP.2010.030538

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Publishing type：Research paper (scientific journal)   Publisher：Inderscience Publishers  

DOI： 10.1504/ijaip.2010.030538

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

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.

DOI： 10.1109/GrC.2010.168

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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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Language：English   Publisher：ELSEVIER SCIENCE INC  

DOI： 10.1016/j.ins.2008.12.007

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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.

DOI： 10.1007/978-3-642-04592-9_34

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

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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

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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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

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

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

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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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Artificial Intelligence  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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

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

DOI： 10.1007/978-3-540-34353-0_2

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

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

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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Grant-in-Aid for Scientific Research on Priority Areas

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

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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

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

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

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

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

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

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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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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Language：English  

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

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Language：Japanese   Publisher：Kobe University  

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学部共同研究費

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Language：Japanese  

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Other Link： http://id.nii.ac.jp/1004/00007950/

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Other Link： http://id.nii.ac.jp/1004/00007690/

J-GLOBAL

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J-GLOBAL

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Other Link： http://id.nii.ac.jp/1004/00007306/

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Language：English   Publisher：World Scientific Publishing Company  

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Language：Japanese   Publisher：人工知能学会  

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Language：Japanese   Publisher：人工知能学会  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

The purpose of this paper is to propose a new system for the effective integration of various and enormous data based on MUSASHI and the discovery of business chance to understand consumer behavior. We employ XML data structure in MUSASHI to integrate such different data as transaction data in companies, communication data of consumers on Internet and research data attained by using mobile network. This paper deals with consumer behavior research systems, called CODIRO, to understand effects on sales from processed food television commercials.

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Language：Japanese   Publisher：The Japanese Society for Artificial Intelligence  

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Language：English   Publisher：Information Processing Society of Japan (IPSJ)  

This paper concerns an actual case of the business application of graph mining techniques to purchase history data that employs the use of an active mining process and the verification of the practical utility of the final results. In the case of data mining processes used up to the present time that involved iteration for analysis under given data, in contrast, active mining is based on user interest levels and both the data and the process evolve dynamically in the form of a spiral configuration. In this paper, we describe the process of how new knowledge concerning consumer behavior in the alcoholic beverage market was discovered and how, based on this, new sales promotion planning was carried out and the effects were verified.

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Other Link： http://id.nii.ac.jp/1001/00050281/

Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

This paper deals with an actual case of the business application of graph mining techniques to pur-chase history data that employs an active mining process and the verification of the practical utility of the final' results. While the current data mining process emphasizes iterative analysis of a given data, active mining is based on user interest levels and both the data and the process evolve dynamically in the.form of a spiral configuration. In this paper, we describe the process of how new knowledge concerning consumer behavior in the alcoholic bev-erage market was discovered and how, based on this, new sales promotion planning was carried out and the effects were verified.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

In this paper we introduce new consumer research systems called CODIRO, which are integrated with mobile network and data mining technology. CODIRO contains the active information gathering systems by using mobile network technology to acquire information of consumer recognition and attitudes about TV commercial. Using CODIRO, we have succeeded in construction of sales promotion model about TV commercial which can deal with detailed variables about consumer behavior.

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Other Link： http://id.nii.ac.jp/1001/00050331/

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Language：Japanese   Publisher：人工知能学会  

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Language：Japanese   Publisher：人工知能学会  

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Language：English   Publisher：人工知能学会  

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Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

MUSASHI is a set of commands that 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 data mining oriented CRM systems based on MUSASHI, which are integrated with the marketing tools and data mining technology. Everybody can construct useful CRM systems at extremely low cost by introducing MUSASHI.

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Language：English   Publisher：Information Processing Society of Japan (IPSJ)  

MUSASHI is a set of commands that 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 data mining oriented CRM systems based on MUSASHI, which are integrated with the marketing tools and data mining technology. Everybody can construct useful CRM systems at extremely low cost by introducing MUSASHI.

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Language：Japanese   Publisher：人工知能学会  

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

In this paper we introduce a KDD system called MUSASHI developed by our group It supports the entire KDD process efficiently and effectively based on data in XML format by providing a set of commands each of which performs a single function MUSASHI can be used independently without other RDBMS technology or KDD software We can efficiently handle large data (more than 4 gigabytes with 20 million records) on a standard PC Using MUSASHI, we have succeeded in discovery of useful business knowledge based on a huge amount of real business data provided by several companies

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Language：Japanese   Publisher：人工知能学会  

CiNii Books

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In this paper we introduce a KDD system called MUSASHI developed by our group. It supports the entire KDD process efficiently and effectively based on data in XML format by providing a set of commands each of which performs a single function. MUSASHI can be used independently without other RDBMS technology or KDD software We can efficiently handle large data (more than 4 gigabytes with 20 million records) on a standard PC. Using MUSASHI, we have succeeded in discovery of useful business knowledge based on a huge amount of real business data provided by several companies.

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Language：Japanese   Publisher：人工知能学会  

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

The purpose of this research is to present knowledge discovery process by applying some demand forecasting models to sales data in super market for constructing the strategic sales promotion. We shall provide a communication link between the marketing practioners and the active mining researchers to introduce the case of the consumer behavior analysis in the instant noodle market. It illustrates how the some models have been applied in practice of sales promotion planning and what advantages they have.

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Other Link： http://id.nii.ac.jp/1001/00050553/

Language：Japanese   Publisher：Institute of Systems, Control and Information Engineers  

DOI： 10.11509/isciesci.46.4_190

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Language：Japanese   Publisher：人工知能学会  

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Language：Japanese   Publisher：Kobe University  

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Language：Japanese   Publisher：公益社団法人日本オペレーションズ・リサーチ学会  

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Other Link： http://id.nii.ac.jp/1004/00005162/

Language：English   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publisher：人工知能学会  

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Language：English   Publisher：大阪産業大学学会  

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

The Purpose of this paper is to present problems of research and education for IT in the future through our joint research projects between firms and university on data mining application. We analyzed the real business data mining technology, for example, "association rule". We insist that it is important for researchers to collaborate with firms in the real world.

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Language：Japanese   Publisher：人工知能学会  

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Event date： 2018.10

Venue：Miyazaki Japan  

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Event date： 2018.10

Venue：Miyazaki Japan  

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Event date： 2018.6

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Event date： 2018.2

Venue：福岡  

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Event date： 2017.12

Venue：コープさっぽろ, 北海道  

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Event date： 2017.12

Venue：Mana Island Resort and Spa, Fiji  

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Event date： 2017.12

Venue：Mana Island Resort and Spa, Fiji  

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Event date： 2017.12

Venue：Mana Island Resort and Spa, Fiji  

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Event date： 2017.10

Venue：Banff, Canada  

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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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Event date： 2017.9

Venue：2017年度第3回オギノFSP研究会, 山梨  

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Event date： 2017.9

Venue：Marseille, France  

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Event date： 2017.7

Venue：APSCIT2017 Hokkaido Japan  

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Event date： 2017.7

Venue：Bangkok, Thailand  

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Event date： 2017.5

Venue：日本流通産業㈱, 大阪  

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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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Event date： 2016.12

Venue：World Trade Center Barcelona, Spain  

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Event date： 2016.12

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

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Event date： 2016.12

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

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Event date： 2016.10

Venue：Beijing, China  

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Event date： 2016.9

Venue：York, United Kingdom  

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Event date： 2016.8

Venue：MISNC 2016, New Jersey USA  

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Event date： 2016.7

Venue：Seoul, Korea  

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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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Event date： 2016.6

Venue：Shanghai, China  

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Event date： 2016.1

Venue：Venice, Italy  

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Event date： 2015.12

Venue：Fiji  

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Event date： 2015.11

Venue：Atlantic City, NJ, USA  

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Event date： 2015.9

Venue：福岡  

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Event date： 2015.9

Venue：東京  

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Event date： 2015.9

Venue：Marina Bay SandsHotel, Singapore  

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Event date： 2015.9

Venue：Marina Bay Sands Hotel, Singapore  

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Event date： 2015.3

Venue：東京  

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Event date： 2015.1

Venue：Paris, France  

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Event date： 2014.11

Venue：東京  

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Event date： 2014.11

Venue：Nadi, Fiji  

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Event date： 2014.10

Venue：San Diego, CA, USA  

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Event date： 2014.9

Venue：Gdynia, Poland  

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Event date： 2014.8

Venue：Singapore  

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Event date： 2014.5

Venue：山梨  

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Event date： 2013.7

Venue：New York, USA  

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Event date： 2011.9

Venue：Kaiserslautern, Germany  

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Event date： 2011.1

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Event date： 2010

Venue：Cardiff, UK  

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Event date： 2007.7

Keynote Speech

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Event date： 2007.6

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Event date： 2007.2

Venue：San Diego  

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Event date： 2006.12

Venue：Hong Kong  

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Event date： 2006.11

Venue：Taipei  

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Event date： 2006.6

Venue：Tokyo  

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Event date： 2006.6

Venue：Tokyo  

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Event date： 2004.6

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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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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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Event date： 2003

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Event date： 2003

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Language：English   Presentation type：Oral presentation (keynote)  

Venue：Budapest, Hungary  

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Country：Japan

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Country：Japan

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Country：Japan

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Country：Japan

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Country：Japan

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Country：Japan

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