kmed - Distance-Based k-Medoids
Algorithms of distance-based k-medoids clustering: simple
and fast k-medoids, ranked k-medoids, and increasing number of
clusters in k-medoids. Calculate distances for mixed variable
data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and
Ahmad-Dey. Cluster validation applies internal and relative
criteria. The internal criteria includes silhouette index and
shadow values. The relative criterium applies bootstrap
procedure producing a heatmap with a flexible reordering matrix
algorithm such as complete, ward, or average linkages. The
cluster result can be plotted in a marked barplot or pca
biplot.
