한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)

한국정보처리학회 (Korea Information Processing Society)
권호 표지

전략적 중요도를 고려한 연관규칙 탐사

Association Rule Mining Considering Strategic Importance

  • 최덕원 (성균관대학교 시스템경영공학과) ;
  • 신진규 (성균관대학교 시스템경영공학과)
  • Choi, Doug-Won (Department of Systems Management Engineering, SungKyunKwan University) ;
  • Shin, Jin-Gyu (Department of Systems Management Engineering, SungKyunKwan University)
  • 발행 : 2007.05.11
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초록

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.

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