Algorithm-Agnostic Feature Attributions for Clustering.

Abstract Understanding how assignments of instances to clusters can be attributed to the features can be vital in many applications. However, research to provide such feature attributions has been limited. Clustering algorithms with built-in explanations are scarce. Common algorithm-agnostic approaches involve dimension reduction and subsequent visualization, which transforms the original features used to cluster the data; or training a supervised learning classifier on the found cluster labels, which adds additional and intractable complexity. We present FACT ( f eature a ttributions for c lus t ering), an algorithm-agnostic framework that preserves the integrity of the data and does not introduce additional models. As the defining characteristic of FACT, we introduce a set of work stages: sampling, intervention, reassignment, and aggregation. Furthermore, we propose two novel FACT methods: SMART ( s coring m etric a fte r permu t ation) measures changes in cluster assignments by custom scoring functions after permuting selected features; IDEA ( i solate d e ffect on a ssignment) indicates local and global changes in cluster assignments after making uniform changes to selected features.