• 제목, 요약, 키워드: 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.

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

  • 김의찬;황병연
    • 정보처리학회논문지D
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    • v.13D no.3
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    • pp.293-298
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    • 2006
  • 데이터베이스에는 많은 데이터들이 저장되어 있다. 무수히 많은 데이터들로부터 어떠한 정보를 얻기 위해서는 질의문을 사용하면 된다. 질의문을 통해 얻는 정보들은 기본적이고 단순한 정보들이다. 데이터 마이닝은 데이터베이스를 통해서 얻을 수 없는 정보를 얻게 해주는 기법이다. 데이터 마이닝 기법에는 여러 가지가 있지만 본 논문에서는 클러스터링과 연관규칙을 찾아내는 기법을 다룬다. 기존의 연관규칙 기법에서의 문제점을 보완하고 더 나은 규칙들을 찾아내기 위한 방법을 제시한다. 여기에 클러스터링 방법을 적용하게 되는데 기존의 거리기반이나 범주 기반 등의 클러스터링이 아닌 연관규칙에 적합한 클러스터링 기법을 제안하여 적용하게 된다. 각 클러스터의 연관규칙들을 찾게 되면 기존의 전체 데이터베이스에서 찾아진 연관규칙 뿐만 아니라 클러스터들의 특징이 될 규칙들도 찾을 수 있게 된다. 본 연구를 통해 대용량 데이터베이스의 많은 트랜잭션 접근을 줄이고 소집단의 연관성도 찾을 수 있다.

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

  • 박세일;이수원
    • 한국정보과학회논문지:소프트웨어및응용
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    • v.29 no.1_2
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    • pp.53-64
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    • 2002
  • 연관규칙 탐사기법은 트랜잭션들을 대상으로 항목간 또는 속성간의 연관관계를 발견하는 방법으로, 데이터 집합의 구조를 쉽게 통찰할 수 있다는 장점으로 인하여 활발히 연구되어 왔다. 그러나 현재까지의 연구들은 전체 사용자 중 공통적인 특성을 지닌 사용자 그룹이 존재할 경우, 이러한 그룹별 연관규칙을 찾아낼 수 없다는 한계점을 지닌다. 본 논문에서는 이러한 점을 해결하기 위하여, 속성선택 및 사용자 구분 기법을 이용하여 사용자를 부분집합으로 구분하고 그 부분집합별로 연관규칙을 발견한다. 또한 위와 같이 얻어진 지역적 연관규칙이 전체 사용자를 대상으로 한 전역적 연관규칙보다 해당 부분집합에 더욱 적합하다는 사실을 여러 연관규칙 평가치를 이용하여 평가한다.

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

  • 이동명;지영근;황종원;강맹규
    • 산업경영시스템학회지
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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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상관관계와 카이-제곱 분석에 기반한 긍정과 부정 연관 규칙 알고리즘 (Mining Positive and Negative Association Rules Algorithm based on Correlation and Chi-squared analysis)

  • 김나희;윤성대
    • 한국정보통신학회:학술대회논문집
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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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대화형 환경에서 효율적인 연관 규칙 알고리즘 (Efficient Algorithms for Mining Association Rules Under the Interactive Environments)

  • 이재문
    • 정보처리학회논문지D
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    • v.8D no.4
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    • pp.339-346
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    • 2001
  • 대화형 환경에서 연관 규칙 탐사 문제는 동일한 데이터베이스에서 다른 최소 지지도로 반복적으로 연관 규칙을 탐사하는 것이다. 이 문제는 반복적으로 연관 규칙을 탐사한다는 사실만 기존의 연관 규칙 탐사와 다를 뿐 기존의 연관 규칙 탐사에서 발생하는 모든 문제를 포함한다. 본 논문은 전 단계에 계산된 후보 항목집합에 대한 정보를 이용함으로써 성능 향상을 가져오는 효율적인 알고리즘을 제안한다. 제안된 알고리즘은 대화형 환경에서 기존의 알고리즘과 수행 시간 측면에서 비교되었다. 성능 비교의 결과로부터 제안하는 알고리즘이 기존의 방법보다 약 10~30% 정도의 상대적 성능 향상 효과가 있음을 알 수 있었다.

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

  • 권명희;김선중
    • 한국주거학회논문집
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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.

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

  • 이종섭;황종원;강맹규
    • 산업경영시스템학회지
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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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연관규칙 탐색에서 새로운 흥미도 척도의 제안 (A New Interestingness Measure in Association Rules Mining)

  • 안광일;김성집
    • 대한산업공학회지
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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.