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To solve the problems of the FCM (outlier sensitivity) and PCM (coincident clusters) clustering algorithms, Pal et al. [33] proposed a hybridized PFCM clustering model, where the function to be optimized is given by Equation 5: J p f c m ( Z ; U, T, V ) = ∑ i = 1 c ∑ k = 1 N a μ i k m + b t i k η × z k - v i 2 + ∑ i = 1 c γ i ∑ k = 1 N ( 1 - t i k ) η, (5).
However, it is well-known that depending on the initial centres of the clusters, clustering results can change significantly.
To test the robustness of the three Ward's clusters, clustering with McQuitty and complete-linkage was also performed, (Fig. S1).
As the clusters were large, with 1938 subjects in 18 clusters, clustering should have been taken into account.
To check the robustness of the clusters, clustering of samples was repeated with the Covariance and Cosine correlation distance metrics.
These networks by nature do not contain clusters; clustering via AllegroMCODE on these networks revealed indeed that no clusters were generated.
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Table 13 IQV rate for each cluster Clustering All systems Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 K-means (5) 0.910 0.867 0.923 0.968 0.886 0.839 Table 14 Descriptive statistics of the internal class attribute metrics (K-means clustering) Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Nb.
Table 16 IQV rate for each cluster Clustering All systems Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 WMC 0.910 0.869 0.941 0.957 0.889 0.672 LOC 0.877 0.943 0.912 0.895 0.768 CBO 0.842 0.946 0.952 0.806 0.762 Table 17 Descriptive statistics of the unit test case metrics (Univariate clustering - LOC) LOC Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Nb.
Using a cluster analysis, the 38 country parks were classified into four clusters (cluster 4 had only one park).
Cluster A showed footprints of introgression from three different clusters (clusters 3, D and 18, Fig. 4a and Supplementary Fig. 5) while cluster B only showed footprints of introgression from cluster 2 (Fig. 4b and Supplementary Fig. 6).
Structure will form on smaller scales first: stars before galaxies, galaxies before clusters, clusters before superclusters.
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