wskm: Weighted k-Means Clustering

Entropy weighted k-means (ewkm) by Liping Jing, Michael K. Ng and Joshua Zhexue Huang (2007) <doi:10.1109/TKDE.2007.1048> 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) by Xiaojun Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013) <doi:10.1109/TKDE.2011.262> 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) by Xiaojun Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012) <doi:10.1016/j.patcog.2011.06.004> extends this concept by grouping features and weighting the group in addition to weighting individual features.

Version: 1.4.40
Depends: R (≥ 2.10), grDevices, stats, lattice, latticeExtra, fpc
Published: 2020-04-05
DOI: 10.32614/CRAN.package.wskm
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_1.4.40.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): wskm_1.4.40.tgz, r-oldrel (arm64): wskm_1.4.40.tgz, r-release (x86_64): wskm_1.4.40.tgz, r-oldrel (x86_64): wskm_1.4.40.tgz
Old sources: wskm archive


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