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Algorithm-Agnostic Feature Attributions for Clustering

MCML Authors

Christian Alexander Scholbeck

Dr.

* Former Member

→ Group Bernd Bischl
Statistical Learning and Data Science

Henri Funk

→ Group Helmut Küchenhoff
Statistical Consulting Unit (StaBLab)

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
Statistical Learning and Data Science

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 (feature attributions for clustering), 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 (scoring metric after permutation) measures changes in cluster assignments by custom scoring functions after permuting selected features; IDEA (isolated effect on assignment) indicates local and global changes in cluster assignments after making uniform changes to selected features.

inproceedings