한국산업정보학회논문지 (Journal of Korea Society of Industrial Information Systems)

한국산업정보학회 (Korea Society of Industrial Information Systems)
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효율적인 연관규칙 감축을 위한 WT-알고리즘에 관한 연구

A Study on WT-Algorithm for Effective Reduction of Association Rules

  • 박진희 (대구한의대학교 교양학부) ;
  • 피수영 (대구가톨릭대학교 교양교육원)
  • 투고 : 2015.03.06
  • 심사 : 2015.10.07
  • 발행 : 2015.10.31
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초록

매일 각종 모바일 디바이스와 온라인, 소셜네트워크서비스 등에서 쏟아지는 데이터로 인해 정보의 홍수를 넘어 과부하 상태에 있다. 이미 생성되어 있는 기존 정보들도 있지만 시시각각 새롭게 생겨나고 있는 정보들이 헤아릴 수 없을 정도이다. 연관분석은 이러한 정보들 속에서 나타나는 항목의 발생 빈도수가 최소 지지도보다 큰 빈발항목집합(Frequent Item set)을 찾는 방법이다. 항목의 수가 많아짐에 따라 규칙의 수도 기하급수적으로 늘어나므로 원하는 정보를 찾기가 어려운 단점이 있다. 따라서 본 논문에서는 트랜잭션데이터 집합을 Boolean 변수 아이템으로 나타내었다. 논리함수를 간소화하는데 사용되는 Quine-McKluskey의 방법으로 알고리즘화하여 각 항목에 가중치를 부여한 WT-알고리즘을 제안한다. 제안한 알고리즘은 항목의 개수와 관계없이 간략화가 가능한 장점으로 인하여 불필요한 규칙을 감소시켜 데이터마이닝 효율을 향상시킬 수 있다.

We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

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참고문헌

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