Generate Heatmaps Based on Partitioning Around Medoids (PAM)
Data are partitioned (clustered) into k clusters "around medoids", which is
a more robust version of K-means implemented in the function pam() in the 'cluster' package.
The PAM algorithm is described in Kaufman and Rousseeuw (1990) <10.1002>.
Please refer to the pam() function documentation for more references.
Clustered data is plotted as a split heatmap allowing visualisation of representative
"group-clusters" (medoids) in the data as separated fractions of the graph while those
"sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.10.1002>