weightedKmeans: Weighted KMeans Clustering

Entropy weighted kmeans (ewkm) is a weighted subspace clustering algorithm that is well suited to very high dimensional data. Weights are calculated as the importance of a variable with regard to cluster membership. The feature group weighted kmenas (fgkm) extends this concept by grouping features and weighting the group in addition to weihgting individual features.

Version: 1.2.0
Depends: lattice, latticeExtra, clv
Published: 2012-04-27
Author: Graham Williams, Joshua Z Huang, Xiaojun Chen, Qiang Wang, Longfei Xiao
Maintainer: Graham Williams <Graham.Williams at togaware.com>
License: GPL (≥ 3) (see file LICENSE)
Copyright: 2011-2012 Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
URL: http://www.siat.ac.cn
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: weightedKmeans results


Reference manual: weightedKmeans.pdf
Package source: weightedKmeans_1.2.0.tar.gz
Windows binaries: r-devel: weightedKmeans_1.2.0.zip, r-release: weightedKmeans_1.2.0.zip, r-oldrel: weightedKmeans_1.2.0.zip
OS X Snow Leopard binaries: r-release: weightedKmeans_1.2.0.tgz, r-oldrel: weightedKmeans_1.2.0.tgz
OS X Mavericks binaries: r-release: weightedKmeans_1.2.0.tgz

Reverse dependencies:

Reverse suggests: rattle