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 validations apply internal and relative criteria. The internal criteria include silhouette index and shadow values. The relative criterium applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as ward, complete, or centroid linkages. The cluster result can be plotted in a marked barplot or pca biplot.

Version: 0.3.0
Depends: R (≥ 2.10)
Imports: ggplot2
Suggests: knitr, rmarkdown
Published: 2019-06-14
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_0.3.0.tar.gz
Windows binaries: r-devel: kmed_0.3.0.zip, r-release: kmed_0.3.0.zip, r-oldrel: kmed_0.3.0.zip
OS X binaries: r-release: kmed_0.3.0.tgz, r-oldrel: kmed_0.3.0.tgz
Old sources: kmed archive

Reverse dependencies:

Reverse imports: condvis2

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