Mining Interesting Correlated Contrast Sets

Abstract

Contrast set mining has been developed as a data mining task which aims at discerning differences across groups. These groups can be patients, organizations, molecules, and even time-lines. A valid correlated contrast set is a conjunction of attribute-value pairs that are highly correlated with each other and differ significantly in their distribution across groups. Although the search for valid correlated contrast sets produces a comparatively smaller set of results than the search for valid contrast sets, these results must still be further filtered in order to be examined by a domain expert and have decisions enacted from them. In this paper, we apply the minimum support ratio threshold which measures the ratio of maximum to minimum support across groups. We propose a contrast set mining technique which utilizes the minimum support ratio threshold to discover maximal valid correlated contrast sets. We also demonstrate how four probability-based objective measures developed for association rules can be used to rank contrast sets. Our experiments on real datasets demonstrate the efficiency and effectiveness of our approach.