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Published VersionCC BY-NC-ND V4.0, Open Access
Preprint
clusterBMA: Bayesian model averaging for clustering
arXiv, Vol.9 September 2022
Cornell University
2022
Abstract
Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble and consensus 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, and assumptions made, especially with small sample size or small cluster sizes. 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 combine 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 a combination of clustering internal validation criteria as a novel approximation of the posterior model probability for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. This method is implemented in an accompanying R package.
We explore the performance of this approach through a case study that aims to to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. We also use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separations between subgroups, and with varying numbers of clusters between models.
Details
- Title
- clusterBMA: Bayesian model averaging for clustering
- Authors
- Owen Forbes (Corresponding Author) - Queensland University of TechnologyEdgar Santos-Fernandez (Author) - Queensland University of TechnologyPaul Pao-Yen Wu (Author) - Queensland University of TechnologyHong-Bo Xie (Author) - Queensland University of TechnologyPaul E Schwenn (Author) - The University of QueenslandJim Lagopoulos (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteLia Mills (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteDashiell D Sacks (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteDaniel F Hermens (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteKerrie Mengersen (Author) - Queensland University of Technology
- Publication details
- arXiv, Vol.9 September 2022
- Publisher
- Cornell University
- Date published
- 2022
- DOI
- 10.48550/arXiv.2209.04117
- ISSN
- 2331-8422
- Copyright note
- This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC BY-NC-ND 4.0) which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.
- Organisation Unit
- Centre for Human Factors and Systems Science; University of the Sunshine Coast, Queensland; Thompson Institute
- Language
- English
- Record Identifier
- 99676397302621
- Output Type
- Preprint
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