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clusterBMA: Bayesian model averaging for clustering
Journal article   Open access   Peer reviewed

clusterBMA: Bayesian model averaging for clustering

Owen Forbes, Edgar Santos-Fernandez, Paul Pao-Yen Wu, Xie Hong-Bo, Paul Schwenn, Jim Lagopoulos, Lia Mills, Dashiell D Sacks, Daniel F Hermens and Kerrie Mengersen
PLoS One, Vol.18(8), pp.1-23
2023
DOI: https://doi.org/10.1371/journal.pone.0288000
PMCID: PMC10441802
PMID: 37603575
Appears in  Thompson Institute Research Collection
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https://doi.org/10.1371/journal.pone.0288000View
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Abstract

Bayesian analysis EEG algorithms allocations approximation case studies clustering electroencephalography mathematical models matrices (mathematics) methods model accuracy modelling parameter sensitivity probability separation simulation statistical analysis subgroups uncertainty Other Collaborations Thompson Institute Special Collection
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.

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