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.

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

Downloads:

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 (arm64): kmed_0.4.0.tgz, r-release (x86_64): kmed_0.4.0.tgz, r-oldrel: kmed_0.4.0.tgz
Old sources: kmed archive

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