Package: wskm
Version: 1.4.28
Date: 2015-07-08
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), grDevices, stats, 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: 2011-2014 Shenzhen Institutes of Advanced Technology Chinese
        Academy of Sciences
LazyLoad: yes
LazyData: yes
URL: https://github.com/SimonYansenZhao/wskm,
        http://english.siat.cas.cn/
BugReports: https://github.com/SimonYansenZhao/wskm/issues
NeedsCompilation: yes
Packaged: 2015-07-08 11:47:00 UTC; simon
Author: Graham Williams [aut],
  Joshua Z Huang [aut],
  Xiaojun Chen [aut],
  Qiang Wang [aut],
  Longfei Xiao [aut],
  He Zhao [cre]
Repository: CRAN
Date/Publication: 2015-07-08 14:46:30
Built: R 3.3.3; x86_64-w64-mingw32; 2018-04-22 15:57:31 UTC; windows
Archs: i386, x64
