# Bayesian Consensus Clustering

arXiv: https://arxiv.org/abs/1302.7280 · HTML: https://mlsanity.com/paper/1302.7280/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2013.

## Built on by (most cited first, 8 of 8)

- [CustOmics: A versatile deep-learning based strategy for multi-omics integration](https://mlsanity.com/paper/2209.05485/) (2022)
- [Adaptive Weighted Multi-View Clustering](https://mlsanity.com/paper/2110.13240/) (2021)
- [Bayesian nonparametric mixture inconsistency for the number of components: How worried should we be in practice?](https://mlsanity.com/paper/2207.14717/) (2022)
- [VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data](https://mlsanity.com/paper/2406.16227/) (2024)
- [Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis](https://mlsanity.com/paper/2203.02794/) (2022)
- [Bayesian outcome-guided multi-view mixture models with applications in molecular precision medicine](https://mlsanity.com/paper/2303.00318/) (2023)
- [Cluster Quilting: Spectral Clustering for Patchwork Learning](https://mlsanity.com/paper/2406.13833/) (2024)
- [Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset](https://mlsanity.com/paper/2411.15180/) (2024)

## Abstract (arXiv)

The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings. We demonstrate that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source separately. This work is motivated by the integrated analysis of heterogeneous biomedical data, and we present an application to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas. Software is available at http://people.duke.edu/~el113/software.html.