• Title, Summary, Keyword: Association rules

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Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies

  • Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.3
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    • pp.210-214
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    • 2002
  • Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

Frequent Itemset Creation using Bit Transaction Clustering in Data Mining (데이터 마이닝에서 비트 트랜잭션 클러스터링을 이용한 빈발항목 생성)

  • Kim Eui-Chan;Hwang Byung-Yeon
    • The KIPS Transactions:PartD
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    • v.13D no.3
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    • pp.293-298
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    • 2006
  • Many data are stored in database. For getting any information from many data, we use the query sentences. These information is basic and simple. Data mining method is various. In this paper, we manage clustering and association rules. We present a method for finding the better association rules, and we solve a problem of the existing association rules. We propose and apply a new clustering method to fit for association rules. It is not clustering of the existing distance basis or category basis. If we find association rules of each clusters, we can get not only existing rules found in all transaction but also rules that will be characteristics of clusters. Through this study, we can expect that we will reduce the number of many transaction access in large databases and find association of small group.

Deriving Local Association Rules by User Segmentation (사용자 구분에 의한 지역적 연관규칙의 유도)

  • Park, Se-Il;Lee, Soo-Wun
    • Journal of KIISE:Software and Applications
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    • v.29 no.1_2
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    • pp.53-64
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    • 2002
  • Association rule discovery is a method that detects associative relationships between items or attributes in transactions. It is one of the most widely studied problems in data mining because it offers useful insight into the types of dependencies that exist in a data set. However, most studies on association rule discovery have the drawback that they can not discover association rules among user groups that have common characteristics. To solve this problem, we segment the set of users into user-subgroups by using feature selection and the user segmentation, thus local association rules in user-subgroup can be discovered. To evaluate that the local association rules are more appropriated than the global association rules in each user-subgroup, derived local association rules are compared with global association rules in terms of several evaluation measures.

An Algorithm for Updating Discovered Association Rules in Data Mining (데이타 마이닝에서 기존의 연관 규칙을 갱신하는 앨고리듬 개발)

  • 이동명;지영근;황종원;강맹규
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.20 no.43
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    • pp.265-276
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    • 1997
  • There have been many studies on efficient discovery of association rules in large databases. However, it is nontrivial to maintain such discovered rules in large databases because a database may allow frequent or occasional updates and such updates may not only invalidate some existing strong association rules but also turn some weak rules into strong ones. The major idea of updating algorithm is to resuse the information of the old large itemsets and to integrate the support information of the new large itemsets in order to substantially reduce the pool of candidate sets to be re-exmained. In this paper, an updating algorithm is proposed for efficient maintenance of discovered assocation rules when new transaction data are added to a transaction database. And superiority of the proposed updating algorithm will be shown by comparing with FUP algorithm that was already proposed.

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Mining Positive and Negative Association Rules Algorithm based on Correlation and Chi-squared analysis (상관관계와 카이-제곱 분석에 기반한 긍정과 부정 연관 규칙 알고리즘)

  • Kim, Na-hee;Youn, Sung-dae
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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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.

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Efficient Algorithms for Mining Association Rules Under the Interactive Environments (대화형 환경에서 효율적인 연관 규칙 알고리즘)

  • Lee, Jae-Moon
    • The KIPS Transactions:PartD
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    • v.8D no.4
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    • pp.339-346
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    • 2001
  • A problem for mining association rules under the interactive environments is to mine repeatedly association rules with the different minimum support. This problem includes all subproblems except on the facts that mine repeatedly association rules with the s믇 database. This paper proposed the efficient algorithms to improve the performance by using the information of the candidate large itemsets which calculate the previous association rules. The proposed algorithms were compared with the conventional algorithm with respect to the execution time. The comparisons show that the proposed algorithms achieve 10∼30% more gain than the conventional algorithm.

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Relation for the Measure of Association and the Criteria of Association Rule in Ordinal Database

  • Park, Hee-Chang;Lee, Ho-Soon
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.207-216
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    • 2005
  • One of the well-studied problems in data mining is the search for association rules. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. There are three criteria of association rule; support, confidence, lift. The goal of association rule mining is to find all the rules with support and confidence exceeding some user specified thresholds. We can know there is association between two items by the criteria of association rules. But we can not know the degree of association between two items. In this paper we examine the relation between the measures of association and the criteria of association rule for ordinal data.

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A Study on Comparing and Analyzing in Standard Management Rules of Rental Housing by the Management Works (관리업무별 임대주택 표준관리규약 비교분석)

  • Kwon, Myoung-Hee;Kim, Sun-Joong
    • Journal of the Korean housing association
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    • v.24 no.3
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    • pp.55-64
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    • 2013
  • The purpose of the study was to make an improvement of residential management regulations so that the inhabitants of rental housing can manage their housing in efficient and transparent manners by comparing and analyzing in standard management rules of rental housing. The research method were as follows: 1) The analysis framework utilized result of preceding research composed 6 types of management works and 36 items of subcategories by researcher 2) The standard management rules of rental housing used analysis utilized to draw up after a revision of Rental Housing Act in 2000 and to operate in management of rental housing now. 3) The analysis method used content analysis. The management rules of rental housing utilized a total of 5 rules such as management rules of SH corporation (2011), LH corporation (2000), peoples' solidarity for participatory democracy (2000), Citizens' Alliance (2000) and Gyeonggi-do (2001). The research result were as follows: 1) Overall, the management rules of rental housing included faithfully works of operation management and administration management 2) The type of operation management regulated centrally items related resident committee. and The type of administration management regulated centrally items related accounting management. 3) The management rules of SH corporation and LH corporation regulated centrally operation management, maintenance management and etiquette for the basic living as compared with the other rules.

Partition Algorithm for Updating Discovered Association Rules in Data Mining (데이터마이닝에서 기존의 연관규칙을 갱신하는 분할 알고리즘)

  • 이종섭;황종원;강맹규
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.23 no.54
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    • pp.1-11
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    • 2000
  • This study suggests the partition algorithm for updating the discovered association rules in large database, because a database may allow frequent or occasional updates, and such update may not only invalidate some existing strong association rules, but also turn some weak rules into strong ones. the Partition algorithm updates strong association rules efficiently in the whole update database reuseing the information of the old large itemsets. Partition algorithms that is suggested in this study scans an incremental database in view of the fact that it is difficult to find the new set of large itemset in the whole updated database after an incremental database is added to the original database. This method of generating large itemsets is different from that of FUP(Fast Update) and KDP(Kim Dong Pil)

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A New Interestingness Measure in Association Rules Mining (연관규칙 탐색에서 새로운 흥미도 척도의 제안)

  • Ahn, Kwang-Il;Kim, Seong-Jip
    • Journal of Korean Institute of Industrial Engineers
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    • v.29 no.1
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    • pp.41-48
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    • 2003
  • In this paper, we present a new measure to evaluate the interestingness of association rules. Ultimately. to evaluate whether a rule is interesting or not is subjective. However, an interestingness measure is useful in that it shows the cause for pruning uninteresting rules statistically or logically. Some interestingness measures have been developed in association rules mining. We present an overview of interestingness measures and propose a new measure. A comparative study of some interestingness measures is made on an example dataset and a real dataset. Our experiments show that the new measure can avoid the discovery of misleading rules.