COSINE: A Vertical Group Difference Approach to Contrast Set Mining

Abstract

Contrast sets have been shown to be a useful mechanism for describing differences between groups. A contrast set is a conjunction of attribute-value pairs that differ significantly in their distribution across groups. These groups are defined by a selected property that distinguishes one from the other (e.g customers who default on their mortgage versus those that don’t). In this paper, we propose a new search algorithm which uses a vertical approach for mining maximal contrast sets on categorical and quantitative data. We utilize a novel yet simple discretization technique, akin to simple binning, for continuous-valued attributes. Our experiments on real datasets demonstrate that our approach is more efficient than two previously proposed algorithms, and more effective in filtering interesting contrast sets.