A parallelizable model-based approach for marginal and multivariate clustering
This work addresses clustering challenges for datasets with varying marginal cluster structures, offering a computationally efficient and parallelizable method, though it appears incremental as it builds on existing model-based approaches.
The paper tackles the problem of model-based clustering's assumption of equal cluster counts per margin by introducing a finite mixture model per margin and a strategy game-inspired algorithm called Reign-and-Conquer, resulting in good performance in numerical experiments on artificial data and application to real datasets.
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.