Better than the best? Answers via model ensemble in density-based clustering

TL;DR

This paper introduces an ensemble approach for density-based clustering that combines multiple models to enhance stability and robustness, leveraging a new density estimator to better identify high-density regions for grouping.

Contribution

It proposes a novel ensemble clustering method in the density-based framework, integrating parametric and nonparametric density estimates for improved partitioning.

Findings

Enhanced stability of clustering results

Improved robustness against model selection

Effective identification of high-density regions

Abstract

With the recent growth in data availability and complexity, and the associated outburst of elaborate modelling approaches, model selection tools have become a lifeline, providing objective criteria to deal with this increasingly challenging landscape. In fact, basing predictions and inference on a single model may be limiting if not harmful; ensemble approaches, which combine different models, have been proposed to overcome the selection step, and proven fruitful especially in the supervised learning framework. Conversely, these approaches have been scantily explored in the unsupervised setting. In this work we focus on the model-based clustering formulation, where a plethora of mixture models, with different number of components and parametrizations, is typically estimated. We propose an ensemble clustering approach that circumvents the single best model paradigm, while improving stability and robustness of the partitions. A new density estimator, being a convex linear combination of the density estimates in the ensemble, is introduced and exploited for group assignment. As opposed to the standard case, where clusters are typically associated to the components of the selected mixture model, we define partitions by borrowing the modal, or nonparametric, formulation of the clustering problem, where groups are linked with high-density regions. Staying in the density-based realm we thus show how blending together parametric and nonparametric approaches may be beneficial from a clustering perspective.