• Title, Summary, Keyword: Association rules

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An Empirical Study of Qualities of Association Rules from a Statistical View Point

  • Dorn, Maryann;Hou, Wen-Chi;Che, Dunren;Jiang, Zhewei
    • Journal of Information Processing Systems
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    • v.4 no.1
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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.

A Clustering Technique Using Association Rules for The Library and Information Science Terminology (연관규칙을 이용한 문헌정보학 전문용어 클러스터링 기법에 관한 연구)

  • Seung, Hyon-Woo;Park, Mi-Young
    • Journal of the Korean Society for Library and Information Science
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    • v.37 no.2
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    • pp.89-105
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    • 2003
  • In this paper, an effective method for clustering terminologies extracted from text is proposed, in order to develope a search engine to extract relevant information from large web documents. To prevent frequency of the meaningless association rules among general terminologies, only useful association rules among terminologies are produced using database tables which consist of domain-specific terminologies. Such association rules are produced by applying the Apriori algorithm after forming transaction units from groups of association rules in a document. A group of association rules produced from a terminology forms in a cluster.

IMTAR: Incremental Mining of General Temporal Association Rules

  • Dafa-Alla, Anour F.A.;Shon, Ho-Sun;Saeed, Khalid E.K.;Piao, Minghao;Yun, Un-Il;Cheoi, Kyung-Joo;Ryu, Keun-Ho
    • Journal of Information Processing Systems
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    • v.6 no.2
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    • pp.163-176
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    • 2010
  • Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

The Difference between Time Management Practices and Self-evaluation of Time Use by Time Rules in Families, Focused on Middle School Students in Japan (부모 자녀 간 시간에 관한 규칙 유무에 따른 일본 중학생의 시간관리 행동 및 시간사용 자기평가의 차이)

  • Lee, Sujin
    • Korean Family Resource Management Association
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    • v.19 no.4
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    • pp.55-70
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    • 2015
  • This study aims to clarify the difference between scores for time management practice and the self-evaluation of time use according to time rules in families. This study used data obtained from the first survey on after-school activities in 2008, conducted by the Benesse Educational Research and Development Institute in Japan. The study sample consists of 3,372 middle school students. First, scores for independence and planning were highest in third grade, yet the score for regularity was also lowest in third grade. There will be different lifestyles even among middle school students of the same grade, so it is necessary to consider their characteristics and family rules. Second, the scores for independence, regularity and planning were lowest in groups whose families had rules about time (time rules for curfew, time rules for watching television, time rules for playing games), but in which those rules were not kept. These results were similar for both boys and girls and show that it is more important keep rules rather than to make rules.

Application of k-means Clustering for Association Rule Using Measure of Association

  • Lee, Keun-Woo;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.3
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    • pp.925-936
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    • 2008
  • An association rule mining finds the relation among each items in massive volume database. In generating association rules, the researcher specifies the measurements randomly such as support, confidence and lift, and produces the rules. The rule is not produced if it is not suitable to the one any condition which is given value. For example, in case of a little small one than the value which a confidence value is specified but a support and lift's value is very high, this rule is meaningful rule. But association rule mining can not produce the meaningful rules in this case because it is not suitable to a given condition. Consequently, we creat insignificant error which is not selected to the meaningful rules. In this paper, we suggest clustering technique to association rule measures for finding effective association rules using measure of association.

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Directed Association Rules Mining and Classification (목표 속성을 고려한 연관규칙과 분류 기법)

  • 한경록;김재련
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.24 no.63
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    • pp.23-31
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    • 2001
  • Data mining can be either directed or undirected. One way of thinking about it is that we use undirected data mining to recognize relationship in the data and directed data mining to explain those relationships once they have been found. Several data mining techniques have received considerable research attention. In this paper, we propose an algorithm for discovering association rules as directed data mining and applying them to classification. In the first phase, we find frequent closed itemsets and association rules. After this phase, we construct the decision trees using discovered association rules. The algorithm can be applicable to customer relationship management.

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A Study for Antecedent Association Rules

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • 한국데이터정보과학회:학술대회논문집
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    • pp.51-57
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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. For example, in politics, a special interest group may want to support a politician who backs their cause. The group would look for a candidate who supports their views and support his election. Once in office, the politician would then conduct policy that supports the interest group.

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Mining Association Rules of Credit Card Delinquency of Bank Customers in Large Databases

  • Lee, Young-Chan;Shin, Soo-Il
    • Journal of Intelligence and Information Systems
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    • v.9 no.2
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    • pp.135-154
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    • 2003
  • Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

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An Algorithm for Mining Association Rules by Minimizing the Number of Candidate 2-Itemset (후보 2-항목집합의 개수를 최소화한 연관규칙 탐사 알고리즘)

  • 황종원;강맹규
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.21 no.48
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    • pp.53-63
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    • 1998
  • Mining for association rules between items in a large database of sales transaction has been described as an important data mining problem. The mining of association rules can be mapped into the problem of discovering large itemsets. In this paper we present an efficient algorithm for mining association rules by minimizing the total numbers of candidate 2-itemset, │C$_2$│. More the total numbers of candidate 2-itemset, less the time of executing the algorithm for mining association rules. The total performance of algorithm depends on the time of finding large 2-itemsets. Hence, minimizing the total numbers of candidate 2-itemset is very important. We have performed extensive experiments and compared the performance of our algorithm with the DHP algorithm, the best existing algorithm.

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Discovery Temporal Association Rules in Distributed Database (분산데이터베이스 환경하의 시간연관규칙 적용)

  • Yan Zhao;Kim, Long;Sungbo Seo;Ryu, Keun-Ho
    • Proceedings of the Korean Information Science Society Conference
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    • pp.115-117
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    • 2004
  • Recently, mining far association rules in distributed database environments is a central problem in knowledge discovery area. While the data are located in different share-nothing machines, and each data site grows by time. Mining global frequent itemsets is hard and not efficient in large number of distributed sewen. In many distributed databases. time component(which is usually attached to transactions in database), contains meaningful time-related rules. In this paper, we design a new DTA(distributed temporal association) algorithm that combines temporal concepts inside distributed association rules. The algorithm confirms the time interval for applying association rules in distributed databases. The experiment results show that DTA can generate interesting correlation frequent itemsets related with time periods.

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