# Distributed Bayesian clustering using finite mixture of mixtures

arXiv: https://arxiv.org/abs/2003.13936 · HTML: https://mlsanity.com/paper/2003.13936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Federated Variational Inference for Bayesian Mixture Models](https://mlsanity.com/paper/2502.12684/) (2025)

## Abstract (arXiv)

In many modern applications, there is interest in analyzing enormous data sets that cannot be easily moved across computers or loaded into memory on a single computer. In such settings, it is very common to be interested in clustering. Existing distributed clustering algorithms are mostly distance or density based without a likelihood specification, precluding the possibility of formal statistical inference. Model-based clustering allows statistical inference, yet research on distributed inference has emphasized nonparametric Bayesian mixture models over finite mixture models. To fill this gap, we introduce a nearly embarrassingly parallel algorithm for clustering under a Bayesian overfitted finite mixture of Gaussian mixtures, which we term distributed Bayesian clustering (DIB-C). DIB-C can flexibly accommodate data sets with various shapes (e.g. skewed or multi-modal). With data randomly partitioned and distributed, we first run Markov chain Monte Carlo in an embarrassingly parallel manner to obtain local clustering draws and then refine across workers for a final clustering estimate based on any loss function on the space of partitions. DIB-C can also estimate cluster densities, quickly classify new subjects and provide a posterior predictive distribution. Both simulation studies and real data applications show superior performance of DIB-C in terms of robustness and computational efficiency.