Model-Based Clustering

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