Model-Based Clustering

Abstract

Clustering is the task of automatically gathering observations into homogeneous groups, where the number of groups is unknown. Through its basis in a statistical modeling framework, model-based clustering provides a principled and reproducible approach to clustering. In contrast to heuristic approaches, model-based clustering allows for robust approaches to parameter estimation and objective inference on the number of clusters, while providing a clustering solution that accounts for uncertainty in cluster membership. The aim of this article is to provide a review of the theory underpinning model-based clustering, to outline associated inferential approaches, and to highlight recent methodological developments that facilitate the use of model-based clustering for a broad array of data types. Since its emergence six decades ago, the literature on model-based clustering has grown rapidly, and as such, this review provides only a selection of the bibliography in this dynamic and impactful field.