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.


kmed 0.2.0

  • Added barplotnum to make a barplot of numerical data set.
  • Added pcabiplot to make biplot of pca class.
  • Added silhoutte to obtain silhoutte index and plot.
  • Added shadow to obtain shadow value index from medoid instead of centroid and its plot.
  • Added two data sets.
  • Fixed cooccur.
  • Fixed clustboot.
  • Fixed fastkmed.
  • Fixed matching.
  • Fixed stepkmed.
  • Fixed rankkmed.
  • Deleted coocurance.
  • Edited NEWS.md.
  • Edited vignette.

kmed 0.1.0

  • Added a NEWS.md file to track changes to the package.
  • Added step k-medoid and rank k-medoid algorithms.
  • Added ahmad and dey distance for mixed variables.
  • Edited vignette.
  • Deprecated coocurance.

kmed 0.0.1

  • Description and version number are fixed.


  • First submitted to cran

Reference manual

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0.4.0 by Weksi Budiaji, 10 months ago

Browse source code at https://github.com/cran/kmed

Authors: Weksi Budiaji

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2

Suggests knitr, rmarkdown

See at CRAN