wskm (1.4.28)

0 users

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.

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 over 4 years ago.

1 previous version



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of wskm yet. Want to be the first? Write one now.

Related packages:(20 best matches, based on common tags.)

Search for wskm on google, google scholar, r-help, r-devel.

Visit wskm on R Graphical Manual.