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.
Package details |
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Author | Weksi Budiaji [aut, cre] |
Maintainer | Weksi Budiaji <budiaji@untirta.ac.id> |
License | GPL-3 |
Version | 0.4.2 |
Package repository | View on CRAN |
Installation |
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