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) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9984&rep=rep1&type=pdf>, 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 untirta.ac.id> |
| 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: kmed_0.2.0.zip, r-release: kmed_0.2.0.zip, r-oldrel: kmed_0.2.0.zip |
| OS X binaries: | r-release: kmed_0.2.0.tgz, r-oldrel: kmed_0.1.0.tgz |
| Old sources: | kmed archive |
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