Journal of the Korean Data Analysis Society

Korean Data Analysis Society (한국자료분석학회)
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Exploration of PIM Based Similarity Measures with PMP as Association Rule Thresholds

부분 주변 비율을 고려한 확률적 흥미도 기반 유사성 측도의 연관성 평가 기준 활용 방안

  • Published : 2012.12.30
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Abstract

Association rule mining is the method to quantify the relationship between a set of items in a large database, and has been applied in various fields like healthcare, insurance, education, and internet shopping mall. There are three primary measures for association rule, support and confidence and lift. Mostly we generate some association rules using confidence. Confidence is the most important measure of these measures, but it is an asymmetric measure and has only positive value. Thus we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure with partially marginal proportions to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure with partially marginal proportions are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure with partially marginal proportions could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values, and select the best similarity measure by probabilistic interestingness measure.

연관성 규칙 탐사는 방대한 양의 데이터베이스에 내재되어 있는 항목들 서로 간의 관련성을 파악하는 것으로 마케팅, 쇼핑몰, 보건 및 의료, 교육 분야 등 현업에 많이 적용되고 있다. 이러한 연관성 규칙을 탐사하기 위해 지지도, 신뢰도, 향상도 등의 연관성 규칙 평가 기준이 활용되고 있다. 이들 중에서 가장 중심이 되는 측도인 신뢰도는 항상 양의 값을 취하는 비대칭적 측도이기 때문에 항목 간에 연관성 규칙을 생성하는 데 여러 가지 어려움이 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 부분주변 비율을 고려한 확률적 흥미도 기반 유사성 측도를 연관성 평가 기준으로 활용하는 방안을 고려하였다. 그 결과, 부분주변 비율을 고려한 확률적 흥미도 기반 유사성 측도 모두가 기존의 연관성 평가 기준과 마찬가지로 연관성의 정도를 파악할 수 있는 동시에 부호를 포함하고 있어서 연관성의 방향도 알 수 있었다.

Keywords

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