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.
| Version: |
1.4.28 |
| Depends: |
R (≥ 2.10), grDevices, stats, lattice, latticeExtra, clv |
| Published: |
2015-07-08 |
| 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 gmail.com> |
| BugReports: |
https://github.com/SimonYansenZhao/wskm/issues |
| License: |
GPL (≥ 3) |
| Copyright: |
2011-2014 Shenzhen Institutes of Advanced Technology Chinese
Academy of Sciences |
| URL: |
https://github.com/SimonYansenZhao/wskm,
http://english.siat.cas.cn/ |
| NeedsCompilation: |
yes |
| Citation: |
wskm citation info |
| Materials: |
ChangeLog |
| CRAN checks: |
wskm results |