• 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.

• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

• The KIPS Transactions:PartD
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• v.17D no.1
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• pp.1-8
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• 2010

Association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from voluminous transactional data. Certainly, one of the major purposes of association rule mining is utilizing the acquired knowledge to provide marketing strategies such as catalogue design, cross-selling and shop allocation. However, this requires too much time and high cost to only extract the actionable and profitable knowledge from tremendous numbers of discovered patterns. In currently available literature, a number of interest measures have been devised to accelerate and systematize the process of pattern evaluation. Unfortunately, most of such measures, including support and confidence, are prone to yielding impractical results because they are calculated only from the sales frequencies of items. For instance, traditional measures cannot differentiate between the purchases in a small basket and those in a large shopping cart. Therefore, some adjustment should be made to the size of market baskets because there is a strong possibility that mutually irrelevant items could appear together in a large shopping cart. Contrary to the previous approaches, we attempted to consider market basket's size in calculating interest measures. Because the devised measure assigns different weights to individual purchases according to their basket sizes, we expect that the measure can minimize distortion of results caused by accidental patterns. Additionally, we performed intensive computer simulations under various environments, and we performed real case analyses to analyze the correctness and consistency of the devised measure.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.137-139
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• 1999

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1175-1182
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• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.157-162
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• 2001

Internet shopping mall is done commercial trade in particular web sites, and zoomed with an important means of trade for the next generation. When users visit shopping mall, the best objective is determined to users by of offering satisfaction and pleasant experience. Therefore. this paper was selected a menu list as an evaluation factor to emphasize a usage convenience of internet shopping mall, and executed a performance and a subjective satisfaction evaluations on a screen design and a structure design. The results of all the performance and the subjective evaluations for the screen design were shown the shortest search times on left type of the main-menu and vertical type of the medium-menu. In the structure design, it was shown that users were the highest satisfaction in type that search product and shopping cart procedure were firstly processed, and the procedure of a shopping membership and paying a bill were secondly processed at the same screen with regard to a comprehensibility and convenience of purchase procedure. It was shown that the user prefered the shopping mall web site to analogy with the real purchase procedure

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1567-1570
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• 2003

장바구니 데이터들로 구성된 다중데이터베이스를 마이닝 하기 위한 선처리 작업으로는 Ideal&Goodness 기법이 있으며, Ideal&Goodness기법은 유사한 항목이 존재하는 데이터베이스간의 식별이 불가능하다는 단점이 있다. 그러므로 본 논문에서 제안하는 기법은 항목으로만 구성된 집합을 생성하여 데이터베이스간의 거리를 측정하고 항목집합간의 식별능력을 향상시키기 위하여 항목과 지지도를 갖는 항목 데이터 집합을 생성하고 지지도에 대한 확률을 계산한 후, 이를 비교 연산하여 가중치를 계산한다. 본 논문에서는 장바구니 분석을 위한 선처리 단계로써 활용 가능한 클러스터링 기법을 제안하며 성능평가를 통하여 데이터베이스간의 우수한 식별 능력을 보인다.

• The Journal of Bigdata
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• v.7 no.1
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• pp.213-224
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• 2022

In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.407-426
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• 2014

This study analyzed the effects of situational factors and perceived uncertainty on purchase delay behaviors of internet consumers. The survey was conducted from internet consumers in the Seoul Metropolitan areas, and 394 responses were used in the data analysis. The results of this study were as follows. First, the negative experience and avoid regrets of the situational factors had a positive impact on overall purchase delay. The time pressure, changeability about purchase, negative experience and avoid regrets had a positive impact on payment stage delay. Also, the time pressure, negative experience and avoid regrets had a positive impact on shopping cart abandonment. Second, all factors of perceived uncertainty had a positive impact on overall purchase delay and payment stage delay. In addition, the information uncertainty and psychological uncertainty had a positive impact on shopping cart abandonment. Therefore, this study is contributing to the diversification of internet study, and it is provide useful information on the customer management and marketing strategy of internet shopping malls.

• Proceedings of the CALSEC Conference
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• 1998.10b
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• pp.391-401
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• 1998

인터넷의 대중화로 전자상거래 시대를 맞이하게 되었다. 현재 전자상거래용 구매자의 전자지갑은 주로 상품의 입수를 위한 대금지불 수단으로 이용되고 있다. 본 논문에서는 현재 발표된 전자상거래용 전자지갑의 현황을 분석하고 그 구조를 파악하여, 이를 토대로 전자상거래 시대에 있어서의 구매자 입장에서의 차세대 전자지갑의 모델을 디자인하고자 한다. 차세대 전자지갑은 개방형 구조이며, 사용자의 의사결정에 도움을 주어야 하고, 개인 Profile 관리기능, 장바구니 기능, 다양한 지불수단제공, Workflow, ERP시스템과의 연계, 편리한 인증관리 기능 등을 가져야 할 것이다. 본 논문에서는 이러한 차세대 전자지갑의 정의, 특징, 기능, 구조 등에 대해서 논할 것이다.