Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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확률적 흥미도를 이용한 유사성 측도의 연관성 평가 기준

Exploration of PIM based similarity measures as association rule thresholds

  • 투고 : 2012.10.22
  • 심사 : 2012.11.12
  • 발행 : 2012.11.30
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⟨ 이전 논문 다음 논문 ⟩

초록

연관성 규칙 기법은 대용량데이터베이스에 있는 항목들 간의 관련성을 수치화 하는 것으로 데이터 마이닝 기법 중에서는 가장 많이 활용되고 있다. 연관성 규칙을 탐사하기 위한 연관성 규칙 평가 기준에는 지지도, 신뢰도, 향상도 등이 있다. 이들 중에서 가장 중심이 되는 신뢰도는 비대칭적 측도일 뿐만 아니라 항상 양의 값만을 취하고 있어서 항목 간에 연관성 규칙을 생성하는 데 여러가지 문제가 존재한다. 이러한 문제를 해결하기 위해 본 논문에서는 확률적 흥미도 측도 기반, 특히 주변 비율을 고려하지 않은 유사성 측도를 연관성 평가 기준으로 적용하는 방안에 대해 연구하였다. 예제에 의한 비교를 통하여 Yule과 Michael의 유사성 계수와 Pearson의 파이 계수는 신뢰도와 동일하게 연관성의 정도를 파악할 수 있는 동시에 부호를 포함하고 있어서 연관성의 방향도 알 수 있었으나, 카이 제곱 통계량 기반 측도들은 항상 양의 값만 나타날 뿐만 아니라 신뢰도와는 변화하는 양상이 다르다는 것을 확인할 수 있었다.

Association rule mining is the method to quantify the relationship between each set of items in a large database. One of the well-studied problems in data mining is exploration for association rules. There are three primary quality measures for association rule, support and confidence and lift. 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 to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure 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.

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참고문헌

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