A parallelizable model-based approach for marginal and multivariate clustering

• URL: http://arxiv.org/abs/2212.04009v1
• Date: Wed, 7 Dec 2022 23:54:41 GMT
• Title: A parallelizable model-based approach for marginal and multivariate clustering
• Authors: Miguel de Carvalho, Gabriel Martos Venturini, Andrej Svetlo\v{s}\'ak
• Abstract summary: This paper develops a clustering method that takes advantage of the sturdiness of model-based clustering. We tackle this issue by specifying a finite mixture model per margin that allows each margin to have a different number of clusters. The proposed approach is computationally appealing as well as more tractable for moderate to high dimensions than a full' (joint) model-based clustering approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper develops a clustering method that takes advantage of the sturdiness of model-based clustering, while attempting to mitigate some of its pitfalls. First, we note that standard model-based clustering likely leads to the same number of clusters per margin, which seems a rather artificial assumption for a variety of datasets. We tackle this issue by specifying a finite mixture model per margin that allows each margin to have a different number of clusters, and then cluster the multivariate data using a strategy game-inspired algorithm to which we call Reign-and-Conquer. Second, since the proposed clustering approach only specifies a model for the margins -- but leaves the joint unspecified -- it has the advantage of being partially parallelizable; hence, the proposed approach is computationally appealing as well as more tractable for moderate to high dimensions than a `full' (joint) model-based clustering approach. A battery of numerical experiments on artificial data indicate an overall good performance of the proposed methods in a variety of scenarios, and real datasets are used to showcase their application in practice.

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