Package: wskm
Version: 1.4.19
Date: 2014-12-26
Title: Weighted k-means Clustering
Authors@R: c(person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
	person("Joshua Z", "Huang", email="zx.huang@szu.edu.cn", role="aut"),
	person("Xiaojun", "Chen", email="xjchen.hitsz@gmail.com", role="aut"),
	person("Qiang", "Wang", role="aut"),
	person("Longfei", "Xiao", role="aut"),
	person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role="cre"))
Maintainer: He Zhao <Simon.Yansen.Zhao@gmail.com>
Depends: R (>= 2.10), lattice, latticeExtra, clv
Description: 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.
License: GPL (>= 3)
Copyright: 2011-2012 Shenzhen Institutes of Advanced Technology Chinese
        Academy of Sciences
LazyLoad: yes
LazyData: yes
URL: http://www.siat.ac.cn
Packaged: 2014-12-26 13:05:31 UTC; simon
Repository: CRAN
Date/Publication: 2014-12-26 14:36:54
Author: Graham Williams [aut],
  Joshua Z Huang [aut],
  Xiaojun Chen [aut],
  Qiang Wang [aut],
  Longfei Xiao [aut],
  He Zhao [cre]
NeedsCompilation: yes
Built: R 3.1.2; x86_64-apple-darwin10.8.0; 2014-12-27 16:42:35 UTC; unix
Archs: wskm.so.dSYM
