wskm (1.4.28)

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Weighted k-Means Clustering.

https://github.com/SimonYansenZhao/wskm
http://english.siat.cas.cn/
http://cran.r-project.org/web/packages/wskm

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.

Maintainer: He Zhao
Author(s): Graham Williams [aut], Joshua Z Huang [aut], Xiaojun Chen [aut], Qiang Wang [aut], Longfei Xiao [aut], He Zhao [cre]

License: GPL (>= 3)

Uses: clv, lattice, latticeExtra
Reverse suggests: rattle

Released about 3 years ago.


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