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This paper develops a clustering method that takes advantage of thesturdiness of model-based clustering, while attempting to mitigate some of itspitfalls. First, we note that standard model-based clustering likely leads tothe same number of clusters per margin, which seems a rather artificialassumption for a variety of datasets. We tackle this issue by specifying afinite mixture model per margin that allows each margin to have a differentnumber of clusters, and then cluster the multivariate data using a strategygame-inspired algorithm to which we call Reign-and-Conquer. Second, since theproposed clustering approach only specifies a model for the margins -- butleaves the joint unspecified -- it has the advantage of being partiallyparallelizable; hence, the proposed approach is computationally appealing aswell as more tractable for moderate to high dimensions than a `full' (joint)model-based clustering approach. A battery of numerical experiments onartificial data indicate an overall good performance of the proposed methods ina variety of scenarios, and real datasets are used to showcase theirapplication in practice.

A parallelizable model-based approach for marginal and multivariate clustering