Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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An Active Candidate Set Management Model on Association Rule Discovery using Database Trigger and Incremental Update Technique

트리거와 점진적 갱신기법을 이용한 연관규칙 탐사의 능동적 후보항목 관리 모델

  • Hwang, Jeong-Hui (Dept. of Computer Science, Chungbuk National University) ;
  • Sin, Ye-Ho (Dept. of Computer Science, Chungbuk National University) ;
  • Ryu, Geun-Ho (Dept. of Computer Science, Chungbuk National University)
  • 황정희 (충북대학교 전자계산학과) ;
  • 신예호 (충북대학교 전자계산학과) ;
  • 류근호 (충북대학교 전자계산학과)
  • Published : 2002.02.01
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Abstract

Association rule discovery is a method of mining for the associated item set on large databases based on support and confidence threshold. The discovered association rules can be applied to the marketing pattern analysis in E-commerce, large shopping mall and so on. The association rule discovery makes multiple scan over the database storing large transaction data, thus, the algorithm requiring very high overhead might not be useful in real-time association rule discovery in dynamic environment. Therefore this paper proposes an active candidate set management model based on trigger and incremental update mechanism to overcome non-realtime limitation of association rule discovery. In order to implement the proposed model, we not only describe an implementation model for incremental updating operation, but also evaluate the performance characteristics of this model through the experiment.

연관규칙 탐사는 지지도와 신뢰도를 바탕으로 연관성 있는 강한 항목들을 탐사한다. 탐사된 연관규칙은 장바구니 분석 등과 같이 전자 상거래 및 대형 소매점 등의 판매 패턴에 대한 분석에 유용하게 적용될 수 있다. 이와 같은 연관규칙 탐사는 대규모로 축적되어 트랜잭션 데이터를 대상으로 하는 기법으로서 대규모 데이터에 대한 반복적 스캔연산을 수반한다. 그러므로 매우 높은 연산 부하를 안고 있으며 이로 인해 동적 환경에서 실시간 제한사항을 탐사에 대한 시도를 하지 못하고 있다. 따라서 이 논문에서는 연관규칙 탐사의 비 실시간적 제한사항을 위하여 트리거와 점진적 갱신 기법을 이용한 능동적 후보항목 관리 모델을 제안하였다. 아울러 제안 모델을 구현하기 위해 점진적 갱신 기법을 이용한 능동적 후보항목 관리 모델을 제한하였다. 아울러 제안 모델을 구현하기 위해 점진적 갱신 연산의 구현 모델을 제시하고 이의 구현 및 실험을 통해 성능 특성을 분석하였다.

Keywords

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