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.
| Version: | 0.4.0 |
| Depends: | R (≥ 2.10) |
| Imports: | ggplot2 |
| Suggests: | knitr, rmarkdown |
| Published: | 2021-01-04 |
| Author: | Weksi Budiaji |
| Maintainer: | Weksi Budiaji <budiaji at untirta.ac.id> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | kmed results |
| Reference manual: | kmed.pdf |
| Vignettes: |
kmed: Distance-Based K-Medoids |
| Package source: | kmed_0.4.0.tar.gz |
| Windows binaries: | r-devel: kmed_0.4.0.zip, r-release: kmed_0.4.0.zip, r-oldrel: kmed_0.4.0.zip |
| macOS binaries: | r-release: kmed_0.4.0.tgz, r-oldrel: kmed_0.4.0.tgz |
| Old sources: | kmed archive |
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