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.

中文翻译：

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 基于模型的聚类


聚类是将观察结果自动收集到同质组中的任务，其中组的数量未知。基于统计建模框架的基础，基于模型的聚类提供了一种有原则且可重复的聚类方法。与启发式方法相比，基于模型的聚类允许对聚类数量进行参数估计和客观推断的稳健方法，同时提供考虑聚类成员资格不确定性的聚类解决方案。本文的目的是回顾支持基于模型的聚类的理论，概述相关的推理方法，并强调最近的方法论发展，这些发展有助于将基于模型的聚类用于广泛的数据类型。自六十年前出现以来，基于模型的聚类文献迅速增长，因此，本文仅提供这个动态且有影响力的领域的参考书目精选。