Explainable cluster analysis: a bagging approach

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.

Figures (6)

• Figure 1: Kernel density estimation of each feature for a simulation of the synthetic dataset. A rugplot of the occurrences colored by true cluster label is shown.
• Figure 2: Comparison of the mean importance of each feature evaluated by our three proposals for different choices of subspace size, over 20 generations of dataset.
• Figure 3: Clustering performance of the proposed method across the three resampling schemes on the Wine dataset. Results are computed in terms of Adjusted Rand and Fowlkes-Mallows index, and averaged over 20 runs on the dataset, for different subspace size choices. The horizontal axis represents the subspace size chosen ($m$).
• Figure 4: Comparison of the mean importance of each feature evaluated by our three proposals for different choices of subspace size, over 20 runs on the Wine dataset.
• Figure 5: Clustering performance of the proposed method across the three resampling schemes on the Breast dataset. Results are computed in terms of Adjusted Rand and Fowlkes-Mallows index, and averaged over 50 runs on the dataset, for different subspace size choices. The horizontal axis represents the subspace size chosen ($m$).