Explainable cluster analysis: a bagging approach

TL;DR

This paper introduces an ensemble clustering method that enhances interpretability by providing feature importance scores, improving stability especially in small or noisy datasets, and offering a unified view of structure and variable relevance.

Contribution

It presents a novel bagging-based clustering framework with feature importance assessment, addressing the explainability gap in traditional clustering methods.

Findings

Improves stability and robustness of clusters in small-sample or noisy data.

Provides interpretable feature importance scores alongside clustering results.

Demonstrates effectiveness on simulated and real-world datasets.

Abstract

A major limitation of clustering approaches is their lack of explainability: methods rarely provide insight into which features drive the grouping of similar observations. To address this limitation, we propose an ensemble-based clustering framework that integrates bagging and feature dropout to generate feature importance scores, in analogy with feature importance mechanisms in supervised random forests. By leveraging multiple bootstrap resampling schemes and aggregating the resulting partitions, the method improves stability and robustness of the cluster definition, particularly in small-sample or noisy settings. Feature importance is assessed through an information-theoretic approach: at each step, the mutual information between each feature and the estimated cluster labels is computed and weighted by a measure of clustering validity to emphasize well-formed partitions, before being aggregated into a final score. The method outputs both a consensus partition and a corresponding measure of feature importance, enabling a unified interpretation of clustering structure and variable relevance. Its effectiveness is demonstrated on multiple simulated and real-world datasets.