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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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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.
Heatmaps of similarity matrices for each clustering algorithm and BMA consensus matrix.
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 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.
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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 Fig 4 presents the cluster allocations generated by clusterBMA, plotted according to each two-dimensional combination of the three retained principal components.