• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.531-541
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• 2001

In this study, we compare the capabilities of the data mining tools of the most updated version objectively and provide the useful information in which enterprises and universities chose them. In particular, we compare the SAS/Enterprise Miner 3.0, SPSS/Clementine 5.2 and IBM/Intelligent Miner 6.1 which are well known and easily gotten.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1284-1290
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• 2004

In market-basket analysis, weighted association rule(WAR) discovery can mine the rules which include more beneficial information by reflecting item importance for special products. However, when items are divided into more than one group and item importance for each group must be measured by different measurement or separately, we cannot directly apply traditional weighted association rule discovery. To solve this problem, we propose a novel methodology to discovery the weighted association rule in this paper In this methodology, the items should be first divided into sub-groups according to the properties of the items, and the item importance is defined or calculated only with the items enclosed to the sub-group. Our algorithm makes qualitative evaluation for network risk assessment possible by generating risk rule set for risk factor using network sorority data, and quantitative evaluation possible by calculating risk value using statistical factors such as weight applied in rule generation. And, It can be widely used for new model of more delicate analysis in market-basket database in which the data items are distinctly separated.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.4
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• pp.309-317
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• 2015

In this paper, we proposed the standard test specification for safety and function of hiking stick for the elderly. We have concluded nine factors representing specification of hiking stick through analysis of hiking patents and research papers, products survey of business market, case studies for damaged hiking stick and expert surveys. To test the factors, we designed three different kinds of apparatus to examine twist resistance, stick and tip durability and stick straightness. The sample of hiking sticks purchased from market based on Naver sales ranking top to fifteenth. As a result, we concluded six-standard test specification based on eccentric load, adjustable parts load, hand strap load, basket load, tip load and pull load of hiking stick.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.223-226
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• 2009

Recently, Mining negative association rules has received some attention and proved to be useful. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. Several algorithms have been proposed. However, there are some questions with those algorithms, for example, misleading rules will occur when the positive and negative rules are mined simultaneously. The chi-squared test that based on the mature theory and Correlation Coefficient can avoid the problem. In this paper, We proposed the algorithm PNCCR based on chi-squared test and correlation is proposed. The experiment results show that the misleading rules are pruned. It suggests that the algorithm is correct and efficient.

• Korean Management Science Review
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• v.32 no.1
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• pp.65-82
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• 2015

Purchase dependence is a frequent phenomenon in retail shops and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g. market basket analysis), it has largely remained unaddressed in operations management. Since purchase dependence is an important factor in designing inventory replenishment policies, this paper demonstrates the means of applying it to the partial backordering inventory model. Through computational analyses, this paper compares the performance of inventory models that either consider or ignore purchase dependence; the results demonstrate that inventory models that ignore purchase dependence incur more average cost per unit time than the model that considers purchase dependence, and the impact of purchase dependence can increase in significance as the item set becomes more closely correlated with regard to order demand.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.275-288
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• 2015

Purchase dependence is a frequent phenomenon in retail shops and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g. market basket analysis), it has largely remained unaddressed in operations management. Since purchase dependence is an important factor in designing inventory replenishment policies, this paper demonstrates the means of applying it to the partial backordering inventory model. Through computational analyses, this paper compares the performance of inventory models that either consider or ignore purchase dependence; the results demonstrate that inventory models that ignore purchase dependence incur more average cost per unit time than the model that considers purchase dependence, and the impact of purchase dependence can increase in significance as the item set becomes more closely correlated with regard to order demand.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.340-342
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• 2002

다중 값이란 속성 값이 집합인 것을 말한다. 즉, 관계형 데이터베이스에서 자료 유형이 집합인 속성을 의미한다. 이러한 다중 값 속성 처리는 기존 데이터마이닝 기술 자체로는 처리한 수 없으며 후처리나 선처리 과정을 이용하여 처리하고 있다. 전처리나 후처리 과정을 통해 처리할 경우 수행과장에 있어 많은 시간이 소요되고 혹은 타당하지 않은 규칙이 생성되는 문제점을 가지고 있다. 특히 연관화 기법 특성상 분석하고자 할 항목이 증가할수록 연관성의 수가 지수(exponential)단위이기 때문에 이를 해결하는데는 상당한 어려움이 따르게 된다. 본 논문에서는 관계형 데이터베이스 테이블 구조에서 데이터 마이닝의 수행을 위한 전처리나 후처리의 과정을 고려하지 않음으로 위에서 언급된 문제점들을 해결하고자 한다. 특히 데이터 변환 작업 없이 정량적(Quantitative)연관 규칙과 연관 규칙(Market Basket Analysis)의 혼합 형태의 규칙을 생성할 수 있게끔 알고리즘을 확장하여 보다 효율적인 규칙이 생성될 수 있도록 한다. 마지막으로 Each Movie 데이터를 사용하여 확장한 알고리즘의 다중 값 속성 처리 방법의 효율성과 타탕성을 검증한다.

• Korean Management Science Review
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• v.33 no.1
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• pp.1-16
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• 2016

Drop-shipping is a commonly adopted online-order fulfillment strategy in the Internet age. In this practice, online retailers leverage the fulfillment capabilities of suppliers to fulfill orders. On the other hand, purchase dependence is a frequent phenomenon and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g. market basket analysis), it has largely remained unaddressed in operations management. This paper develops an (R, T) model to address an environment in which unmet demand orders are partially lost and partially backordered when purchase dependence exists. The partial backorders are fulfilled by a drop-shipping option. Through computational analyses, this paper demonstrates the effect of both drop shipping on a partial backordering and purchase dependence. The results shows that more profit can be realized by utilizing a drop-shipping option under purchase dependence.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

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. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.