Flexibly Mining Better Subgroups

Hoang Vu Nguyen, Jilles Vreeken

Abstract: In subgroup discovery, perhaps the most crucial task is to discover high-quality one-dimensional subgroups, and refinements of these. For nominal attributes, finding such binary features is relatively straightforward, as we can consider individual attribute values as such. For numerical attributes, the task is more challenging as individual numeric values are not reliable statistics. Instead, we can consider combinations of adjacent values, i.e. bins. Existing binning strategies, however, are not tailored for subgroup discovery. That is, the bins they construct do not necessarily facilitate the discovery of high-quality subgroups, therewith potentially degrading the mining result. To address this, we introduce FLEXI. In short, we propose to use an optimal binning strategy for finding high-quality binary features for both numeric and ordinal attributes. We instantiate FLEXI with various quality measures and show how to achieve efficiency accordingly. Experiments on both synthetic and real-world data sets show that FLEXI outperforms state of the art with up to 25 times improvement in subgroup quality.