한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제18권9호
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  • Pages.177-187
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  • 2013
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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유용한 연관 규칙 추출을 위한 시각적 탐색 기반 접근법

Visual Exploration based Approach for Extracting the Interesting Association Rules

  • 김준우 (동아대학교 산업경영공학과) ;
  • 강현경 (신라대학교 치위생학과)
  • Kim, Jun-Woo (Dept. of Industrial and Management Systems Engineering, Dong-A University) ;
  • Kang, Hyun-Kyung (Division of Dental Hygiene, Silla University)
  • 투고 : 2013.07.05
  • 심사 : 2013.08.02
  • 발행 : 2013.09.30
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초록

연관 규칙 탐사는 다양한 분야에서 널리 쓰이는 데이터 마이닝 기법으로 트랜잭션 데이터에 포함된 이산적인 항목들 간의 인과관계를 추출하는데 목적을 둔다. 하지만 분석자들은 때로 방대한 양의 데이터에서 추출된 많은 연관규칙들을 해석하고 활용하는데 곤란을 겪기도 한다. 이러한 문제점을 해결하기 위하여 본 논문에서는 주어진 트랜잭션 데이터에서 유용한연관 규칙을 탐색하기 위한 새로운 방법인 HTM 접근법을 제안하고자 한다. HTM 접근법은 크게 계층 군집, 테이블 뷰 및 모자이크 플롯의 세 가지 단계로 구성되며, 각 단계는 분석자들에게 적절한 시각적 표현을 제공한다. 예시를 위해 본 논문에서는 상기 접근법을 건강 검진 결과 데이터 분석에 적용하였으며, 실험결과 HTM 접근법을 통해 분석자들은 유용한 규칙들을 보다 효과적으로 탐색할 수 있을 것으로 기대된다.

Association rule mining is a popular data mining technique with a wide range of application domains, and aims to extract the cause-and-effect relations between the discrete items included in transaction data. However, analysts sometimes have trouble in interpreting and using the plethora of association rules extracted from a large amount of data. To address this problem, this paper aims to propose a novel approach called HTM for extracting the interesting association rules from given transaction data. The HTM approach consists of three main steps, hierarchical clustering, table-view, and mosaic plot, and each step provides the analysts with appropriate visual representation. For illustration, we applied our approach for analyzing the mass health examination data, and the result of this experiment reveals that the HTM approach help the analysts to find the interesting association rules in more effective way.

키워드

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