# Explainable cluster analysis: a bagging approach

arXiv: https://arxiv.org/abs/2603.19840 · HTML: https://mlsanity.com/paper/2603.19840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Review of Feature Selection Methods Based on Mutual Information](https://mlsanity.com/paper/1509.07577/) (2015)
- [Bayesian cluster analysis: Point estimation and credible balls](https://mlsanity.com/paper/1505.03339/) (2015)
- [From A-to-Z Review of Clustering Validation Indices](https://mlsanity.com/paper/2407.20246/) (2024)
- [On Rank Graduation Metrics for High Dimensional Ordinal Data](https://mlsanity.com/paper/2511.23100/) (2025)
- [Asymptotic Behavior of Mean Partitions in Consensus Clustering](https://mlsanity.com/paper/1512.06061/) (2015)

## Abstract (arXiv)

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.