Files and links (2)
Published VersionCC BY V4.0, Open Access
Journal article
clusterBMA: Bayesian model averaging for clustering
PLoS One, Vol.18(8), pp.1-23
2023
PMID: 37603575
Appears in Thompson Institute Research Collection
Abstract
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 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. 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. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.
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 TechnologyXie Hong-Bo (Author) - Queensland University of TechnologyPaul 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
- PLoS One, Vol.18(8), pp.1-23
- Publisher
- Public Library of Science
- Date published
- 2023
- DOI
- 10.1371/journal.pone.0288000
- ISSN
- 1932-6203
- PMID
- 37603575
- Copyright note
- © 2023 Forbes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Grant note
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, CE140100049.
- Organisation Unit
- Centre for Human Factors and Systems Science; Thompson Institute
- Language
- English
- Record Identifier
- 99749098902621
- Output Type
- Journal article
Metrics
82 Record Views