A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm

TL;DR

This paper introduces the Bayesian Bagged Clustering (BBC) algorithm, which combines k-means and the proper Bayesian bootstrap to improve clustering robustness, interpretability, and uncertainty estimation, demonstrated on simulated data.

Contribution

The paper proposes a novel ensemble clustering method using the proper Bayesian bootstrap to enhance robustness and interpretability over traditional approaches.

Findings

Improved robustness and interpretability in clustering results.

Provides clear indication of the optimal number of clusters.

Demonstrates methodological and empirical advances on simulated data.

Abstract

The paper presents a novel approach for unsupervised techniques in the field of clustering. A new method is proposed to enhance existing literature models using the proper Bayesian bootstrap to improve results in terms of robustness and interpretability. Our approach is organized in two steps: k-means clustering is used for prior elicitation, then proper Bayesian bootstrap is applied as resampling method in an ensemble clustering approach. Results are analyzed introducing measures of uncertainty based on Shannon entropy. The proposal provides clear indication on the optimal number of clusters, as well as a better representation of the clustered data. Empirical results are provided on simulated data showing the methodological and empirical advances obtained.