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case_images: 2D scatter plots of individuals by principal component scores, coloured by cluster membership, Km = 5.. Top row = k-means; middle row = HC; bottom row = GMM. Left column = PC1 v PC2; middle column = PC1 v PC3; right column = PC2 v PC3.
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case_images: For each algorithm, cluster labels (1-5) have been assigned by decreasing cluster size except for HC clusters 3 and 4, for which labels were switched for the sake of clearer visual comparison across plots in Fig 2. Fig 2 presents the clustering results from each algorithm, plotted according to each two-dimensional combination of the three retained principal components.
case_images: Heatmaps of similarity matrices for each clustering algorithm and BMA consensus matrix.
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case_images:  Fig 3 displays heatmaps of similarity matrices representing results from each of these algorithms, and also indicates the corresponding approximate posterior model probability, acting as a normalised weight  for each model calculated from Eq 13 using the CH index. Fig 3. Finally, we applied SSMF to the consensus matrix C with KBMA = 5 to generate a matrix ABMA of final cluster allocation probabilities.
case_images: 2D scatter plots of individuals by principal component scores, coloured by final cluster membership.. Point size is proportional to the uncertainty of cluster allocation, . Larger points have greater uncertainty.
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case_images:  Fig 4 presents the cluster allocations generated by clusterBMA, plotted according to each two-dimensional combination of the three retained principal components.