# clusterBMA: Bayesian model averaging for clustering

arXiv: https://arxiv.org/abs/2209.04117 · HTML: https://mlsanity.com/paper/2209.04117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 78 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [On the marginal likelihood and cross-validation](https://mlsanity.com/paper/1905.08737/) (2019)
- [Bayesian model averaging: A systematic review and conceptual classification](https://mlsanity.com/paper/1509.08864/) (2015)
- [Bayesian Distance Clustering](https://mlsanity.com/paper/1810.08537/) (2018)
- [Cluster validation by measurement of clustering characteristics relevant to the user](https://mlsanity.com/paper/1703.09282/) (2017)
- [Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification](https://mlsanity.com/paper/1903.09029/) (2019)

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

- [Generalization of generative model for neuronal ensemble inference method](https://mlsanity.com/paper/2211.05634/) (2022)
- [Selective Hypergraph Refinement for Frozen Graph Clustering](https://mlsanity.com/paper/2609.03265/) (2026)

## Abstract (arXiv)

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one `best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a consensus matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name.