[2603.19840] Explainable cluster analysis: a bagging approach

Statistics > Machine Learning arXiv:2603.19840 (stat) [Submitted on 20 Mar 2026] Title:Explainable cluster analysis: a bagging approach Authors:Federico Maria Quetti, Elena Ballante, Silvia Figini, Paolo Giudici View a PDF of the paper titled Explainable cluster analysis: a bagging approach, by Federico Maria Quetti and 3 other authors View PDF HTML (experimental) 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 effectiv...