A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm
This work addresses clustering uncertainty and interpretability for data analysts, but it appears incremental as it builds on existing ensemble and Bayesian methods.
The paper tackled the problem of improving clustering robustness and interpretability by proposing a Bayesian Bagged Clustering algorithm that uses k-means for prior elicitation and the proper Bayesian bootstrap for resampling, resulting in clear indications of optimal cluster numbers and better data representation as shown on simulated data.
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.