wskm: Weighted k-Means Clustering

Entropy weighted k-means (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 two-level variable weighting clustering algorithm tw-k-means (twkm) introduces two types of weights, the weights on individual variables and the weights on variable groups, and they are calculated during the clustering process. The feature group weighted k-means (fgkm) extends this concept by grouping features and weighting the group in addition to weighting individual features.

Version: 1.4.28
Depends: R (≥ 2.10), grDevices, stats, lattice, latticeExtra, clv
Published: 2015-07-08
Author: Graham Williams [aut], Joshua Z Huang [aut], Xiaojun Chen [aut], Qiang Wang [aut], Longfei Xiao [aut], He Zhao [cre]
Maintainer: He Zhao <Simon.Yansen.Zhao at>
License: GPL (≥ 3)
Copyright: 2011-2014 Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
NeedsCompilation: yes
Citation: wskm citation info
Materials: ChangeLog
CRAN checks: wskm results


Reference manual: wskm.pdf
Package source: wskm_1.4.28.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: wskm_1.4.28.tgz
OS X Mavericks binaries: r-oldrel: wskm_1.4.28.tgz
Old sources: wskm archive

Reverse dependencies:

Reverse suggests: rattle


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