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 validation applies internal and
relative criteria. The internal criteria includes silhouette index and shadow
values. The relative criterium applies bootstrap procedure producing a heatmap
with a flexible reordering matrix algorithm such as complete, ward, or average
linkages. The cluster result can be plotted in a marked barplot or pca biplot.
| Version: |
0.4.2 |
| Depends: |
R (≥ 2.10) |
| Imports: |
ggplot2 |
| Suggests: |
knitr, rmarkdown |
| Published: |
2022-08-29 |
| Author: |
Weksi Budiaji [aut, cre] |
| Maintainer: |
Weksi Budiaji <budiaji at untirta.ac.id> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
kmed results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=kmed
to link to this page.