• Journal of Families and Better Life
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• v.14 no.4
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• pp.27-40
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• 1996

The wide range of price found in food market allows consumer reduce the cost of food purchases through comparison shopping into one more stores. Littles known however about how much can be saved by comapring price for a whole market basket of food items. This paper present evidence relating to the comparison shopping through the theories on price is dispersion and show validity constructing methods of food market basket. It is found that the savings of comparison shopping to consumers are likely to be gains in food Markets and three market basket on foods suggest in study found to be validity.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.179-189
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• 2007

Every purchase a customer makes builds patterns about how products are purchased together. The process of finding these patterns, called market basket analysis, is composed of two steps in the Microsoft Association Algorithm. The first step is to find frequent item-sets. The second step which requires much less time than the first step does is to generate association rules based on frequent item-sets. Even though the first step, finding frequent item-sets, is the core part of market basket analysis, when applied to Online Analytical Processing(OLAP) cubes it always raises several points such as longitudinal analysis becomes impossible and many unpractical transactions are built up. In this paper, a new OLAP cube structures designing method which makes longitudinal analysis be possible and also makes only real customers' purchase patterns be identified is proposed for market basket analysis.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• Asian Journal of Business Environment
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• v.5 no.1
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• pp.37-41
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• 2015

Purpose - The study was designed to explore and examine the business relationships of the owners and the employees of Market Basket to analyze the implications of their recent turbulence and decisions. This article focused on two issues - business profit and labor union - to describe the uniqueness of this case. Design, methodology, data, and approach - This article, based on its purpose, applied all three approaches of case studies that are identified and described by Stake (1995), instrumental, intrinsic, and collective, to present the core nature of the issue and to improve and gain a clear understanding of this particular phenomenon. Results - The analysis of this case clearly indicates that seemingly dichotomous concepts of profit and employee welfare are not necessarily antithetical to each other Conclusions - The instant case of Market Basket serves as a testimonial for the rejection of the basic premises of corporate profits and labor unions. This case serves as a model and a practical example for many large retailers, especially the family operated retailers, and workers throughout the world.

• Journal of Information Technology Applications and Management
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• v.9 no.4
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• pp.65-78
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• 2002

We propose a new application method of the datamining technique that might help building an efficient trade strategy in the stock market, where the analysis of the huge database is essential. The proposed method utilizes the association rules among the price changes of individual stock from the market basket analysis (a datamining technique typically used in the Marketing field) in building the strategy We also apply the proposed method to the daily stock prices in Korean stock market, from Jan. 2000 to Dec. 2001. The application results show that the proposed method gives an significantly higher yield rate than the actual stock chage rate.

• Journal of the Korean Home Economics Association
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• v.31 no.2
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• pp.15-25
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• 1993

This article studied on price dispersion of food market in Kwangju. Both item and market basket price dispersion were surveyed, and this price dispersion was compared with the perceptive price dispersion. This survey was conducted from April. 8.1992 to 22. On findings, price dispersion is very big in item and market basket. Most Consumers' perception was inaccurate and typically underestimated. This findings suggest that consumers are victims of contributors to informally imperfect markets.

• Journal of Distribution Science
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• v.21 no.4
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• pp.121-129
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• 2023

Purpose: Market basket analysis is one of the most frequently used methods in the retail industry today as a technique to discover the product association. It is empirically analyzed how these product associations differ regionally in the case of the United States. Research design, data, and methodology: Based on the purchasing data of consumer panels collected from 49 US states, the association rules for each state was extracted with the corresponding lift values indicating product association. The difference in lift values in 49 states by the association rule was compared and tested for 49 states and for 4 census regions (Northeast, Midwest, South, West). Results: The association rules of 3/4 of the same association rules show positive associations or negative associations depending on the lift values of the states. There were significant differences in the lift values for 49 states, and for 4 census regions. These significant differences in the lift values were found to be related to the distance between states and whether states belong to the same census region. Conclusions: Retail product associations shown by market basket analysis may vary depending on regional distance or regional heterogeneity. It is necessary to pay attention to these points in multi-store environment.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.52-59
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• 2018

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Agribusiness and Information Management
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• v.4 no.2
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• pp.14-21
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• 2012

Since the Free Trade Agreements (FTAs) with Chile, the EU, and the U.S., Korean agricultural produce markets have turned into a fierce competition landscape. Under these competitive circumstances, marketing is critical. The objective of the research presented herein is to understand the characteristics of customer preferences after locating trends of purchased items. So This research establishes sustainable strategies for Korean agricultural produce. This investigation used market-basket analysis techniques and panel data for its research. Market-basket analysis is a technique which attempts to find groups of items that are commonly found together. The results show that, for one year, processed food using wheat, processed marine products, and pork are commonly bought together and that yogurt and milk also are bought together. The characteristics of customers buying these items are 44 years old and live in a four-person household with two children. These customers do not live with their parents.