Balancing the Tradeoff Between Clustering Value and Interpretability

TL;DR

This paper introduces a tunable clustering algorithm that balances the tradeoff between cluster quality and interpretability by ensuring a fraction of nodes in each cluster share the same feature, with theoretical analysis and empirical validation.

Contribution

It proposes a novel $eta$-interpretable clustering algorithm that guarantees interpretability constraints and provides efficient solutions and theoretical guarantees for different scenarios.

Findings

The algorithms produce more interpretable clusters in real-world datasets.

The $eta$ parameter effectively controls the interpretability level.

The approach includes generating simple explanations for the clusters.

Abstract

Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This paper addresses the problem of generating interpretable clusters, given features of interest that signify interpretability to an end-user, by optimizing interpretability in addition to common clustering objectives. We propose a $\beta$-interpretable clustering algorithm that ensures that at least $\beta$ fraction of nodes in each cluster share the same feature value. The tunable parameter $\beta$ is user-specified. We also present a more efficient algorithm for scenarios with $\beta\!=\!1$ and analyze the theoretical guarantees of the two algorithms. Finally, we empirically demonstrate the benefits of our approaches in generating interpretable clusters using four real-world datasets. The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.