• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1077-1083
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• 2006

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. In this paper we present association rule mining based antecedent variables. We call these rules to antecedent association rules. An antecedent variable is a variable that occurs before the independent variable and the dependent variable.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.177-188
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• 2005

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.149-160
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• 2006

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• 산업경영시스템학회지
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• 제22권50호
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• pp.183-193
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• 1999

The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In this paper, we propose an efficient algorithm for mining quantitative association rules with item constraints. For categorical attributes, we map the values of the attribute to a set of consecutive integers. For quantitative attributes, we can partition the attribute into values or ranges. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items containing quantitative attributes into the association discovery algorithm using Apriori concept.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1269-1279
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• 2008

Data mining is the technique to find useful information in huge databases. One of the well-studied problems in data mining is exploration for association rules. An association rule technique finds the relation among each items in massive volume databases by several interestingness measures. An important and useful classification scheme of interestingness measures may be based on user-involvement. This results in two categories - objective and subjective measures. This paper present some relative interestingess measures to compare with degrees of association for two groups. A comparative study with some relative interestingness measures is shown by numerical example. The results show that the relative net confidence is the best relative interestingness measure.

• Journal of Information Processing Systems
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• 제4권1호
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• pp.27-32
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• 2008

Minimum support and confidence have been used as criteria for generating association rules in all association rule mining algorithms. These criteria have their natural appeals, such as simplicity; few researchers have suspected the quality of generated rules. In this paper, we examine the rules from a more rigorous point of view by conducting statistical tests. Specifically, we use contingency tables and chi-square test to analyze the data. Experimental results show that one third of the association rules derived based on the support and confidence criteria are not significant, that is, the antecedent and consequent of the rules are not correlated. It indicates that minimum support and minimum confidence do not provide adequate discovery of meaningful associations. The chi-square test can be considered as an enhancement or an alternative solution.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.115-128
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• 2002

One of the well-studied problems in data mining is the search for association rules. In this paper we consider the statistical decision making of association threshold in association rule. A chi-squared statistic is used to find minimum association threshold. We calculate the range of the value that two item sets are occurred simultaneously, and find the minimum confidence threshold values.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2002년도 춘계학술대회
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• pp.169-182
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• 2002

One of the well-studied problems in data mining is the search for association rules. In this paper we consider the statistical decision making of association threshold in association rule. A chi-squared statistic is used to find minimum association threshold. We can calculate the range of the value that two item sets are occurred simultaneously, and can find the minimum confidence threshold values.

• 한국전자통신학회논문지
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• 제9권3호
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• pp.359-368
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• 2014

최근에는 트랜잭션들 사이의 문맥을 반영하기 위해, 단위 트랜잭션들 사이의 관계를 반영한 확장 트랜잭션을 생성하고 이를 대상으로 인터트랜잭션들에 대한 연관 규칙 탐사방안이 연구되었다. 본 연구에서는 기존 인터트랜잭션들에 대한 연관규칙 탐사 기법에 존재하는 두 가지 문제를 제시하였고 이를 해결하기 위한 방안을 제안하였다. 첫째, 인접한 트랜잭션들 상에 존재하는 데이터의 의미적 변화 정보를 반영하기 위한 방안을 제안했다. 둘째, 트랜잭션을 인터트랜잭션으로 변환하는 과정에서 발생하는 불공정 고려를 해결하기 위한 방안을 제안했다. 이를 통해 기존 연구보다 의미 있는 규칙을 생성할 수 있다. 이를 해양 환경 데이터를 기반으로 실험하여 제시한다.