kmed: Distance-Based k-Medoids

Algorithms of distance-based k-medoids clustering: simple and fast k-medoids (Park and Jun, 2009) <doi:10.1016/j.eswa.2008.01.039>, ranked k-medoids (Zadegan et al., 2013) <doi:10.1016/j.knosys.2012.10.012>, and step k-medoids (Yu et al., 2018) <doi:10.1016/j.eswa.2017.09.052>. Calculate distances for mixed variable data such as Gower, Podani, Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, Huang (1997) <>, Harikumar and PV (2015) <doi:10.1016/j.procs.2015.10.077>, and Ahmad and Dey (2007) <doi:10.1016/j.datak.2007.03.016>. Cluster validation applies bootstrap procedure producing a heatmap with a flexible reordering matrix algorithm such as complete, ward, or average linkages.

Version: 0.2.0
Depends: R (≥ 2.10)
Imports: ggplot2
Suggests: knitr, rmarkdown
Published: 2019-01-02
Author: Weksi Budiaji
Maintainer: Weksi Budiaji <budiaji at>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: kmed results


Reference manual: kmed.pdf
Vignettes: K-medoids Distance-Based clustering
Package source: kmed_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: kmed_0.2.0.tgz, r-oldrel: kmed_0.1.0.tgz
Old sources: kmed archive


Please use the canonical form to link to this page.