Explaining Deviating Subsets Through Explanation Networks

Abstract

We propose a novel approach to finding explanations of deviating subsets, often called subgroups. Existing approaches for subgroup discovery rely on various quality measures that nonetheless often fail to find subgroup sets that are diverse, of high quality, and most importantly, provide good explanations of the deviations that occur in the data. To tackle this issue we introduce explanation networks, which provide a holistic view on all candidate subgroups and how they relate to each other, offering elegant ways to select high-quality yet diverse subgroup sets. Explanation networks are constructed by representing subgroups by nodes and having weighted edges represent the extent to which one subgroup explains another. Explanatory strength is defined by extending ideas from database causality, in which interventions are used to quantify the effect of one query on another. Given an explanatory network, existing network analysis techniques can be used for subgroup discovery. In particular, we study the use of Page-Rank for pattern ranking and seed selection (from influence maximization) for pattern set selection. Experiments on synthetic and real data show that the proposed approach finds subgroup sets that are more likely to capture the generative processes of the data than other methods.