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 |
| 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 binaries: | r-release: kmed_0.0.1.tgz, r-oldrel: kmed_0.0.1.tgz |
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