# Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes

arXiv: https://arxiv.org/abs/2401.16708 · HTML: https://mlsanity.com/paper/2401.16708/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Adjusting for Chance Clustering Comparison Measures](https://mlsanity.com/paper/1512.01286/) (2015)

## Built on by (most cited first, 2 of 2)

- [Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures](https://mlsanity.com/paper/2502.19938/) (2025)
- [SCPP: A Unified Python Library for Soft Clustering](https://mlsanity.com/paper/2607.19620/) (2026)

## Abstract (arXiv)

This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible probability density function of the multivariate beta distribution. We introduce the properties of MBMM, describe the parameter learning procedure, and present the experimental results, showing that MBMM fits diverse cluster shapes on synthetic and real datasets. The code is released anonymously at https://github.com/hhchen1105/mbmm/.