kmed: Distance-Based k-Medoids

A simple and fast distance-based k-medoids clustering algorithm from Park and Jun (2009) <doi:10.1016/j.eswa.2008.01.039>. Calculate distances for mixed variable data such as Gower (1971) <doi:10.2307/2528823>, Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, Podani (1999) <doi:10.2307/1224438>, Huang (1997) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9984&rep=rep1&type=pdf>, and Harikumar and PV (2015) <doi:10.1016/j.procs.2015.10.077>. Cluster validation applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages.

Version: 0.0.1
Depends: R (≥ 2.10)
Imports: ggplot2
Suggests: knitr, rmarkdown
Published: 2018-02-12
Author: Weksi Budiaji
Maintainer: Weksi Budiaji <budiaji at untirta.ac.id>
License: GPL-3
NeedsCompilation: no
CRAN checks: kmed results

Downloads:

Reference manual: kmed.pdf
Vignettes: K-medoids Distance-Based clustering
Package source: kmed_0.0.1.tar.gz
Windows binaries: r-devel: kmed_0.0.1.zip, r-release: kmed_0.0.1.zip, r-oldrel: kmed_0.0.1.zip
OS X El Capitan binaries: r-release: kmed_0.0.1.tgz
OS X Mavericks binaries: r-oldrel: not available

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