# A parallelizable model-based approach for marginal and multivariate clustering

arXiv: https://arxiv.org/abs/2212.04009 · HTML: https://mlsanity.com/paper/2212.04009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Mixtures of Experts Models](https://mlsanity.com/paper/1806.08200/) (2018)
- [On choosing mixture components via non-local priors](https://mlsanity.com/paper/1604.00314/) (2016)

## Abstract (arXiv)

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.