wskm: 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.

Author
Graham Williams [aut], Joshua Z Huang [aut], Xiaojun Chen [aut], Qiang Wang [aut], Longfei Xiao [aut], He Zhao [cre]
Date of publication
2015-07-08 14:46:30
Maintainer
He Zhao <Simon.Yansen.Zhao@gmail.com>
License
GPL (>= 3)
Version
1.4.28
URLs

View on CRAN

Man pages

ewkm
Entropy Weighted K-Means
fgkm
Feature Group Weighting K-Means for Subspace clustering
fgkm.sample
Sample dataset to illustrate the fgkm algorithm.
plot.ewkm
Plot Entropy Weighted K-Means Weights
predict.ewkm
Predict method for 'ewkm' model.
twkm
Two-level variable weighting clustering
twkm.sample
Sample dataset to test the twkm algorithm.

Files in this package

wskm
wskm/inst
wskm/inst/CITATION
wskm/inst/ChangeLog
wskm/tests
wskm/tests/Makefile
wskm/tests/fgkm_test.R
wskm/tests/twkm_test.Rout.save
wskm/tests/fgkm_test.Rout.save
wskm/tests/twkm_test.R
wskm/tests/ewkm01.Rout.save
wskm/tests/ewkm01.R
wskm/tests/ewkm01.csv
wskm/src
wskm/src/Utils.h
wskm/src/fgkm.c
wskm/src/twkm.c
wskm/src/Utils.c
wskm/src/ewkm.c
wskm/NAMESPACE
wskm/data
wskm/data/multiplefeatures_remove_pix.RData
wskm/data/datalist
wskm/data/fgkm.sample.RData
wskm/R
wskm/R/predict.ewkm.R
wskm/R/ewkm.R
wskm/R/plot.ewkm.R
wskm/R/fgkm.R
wskm/R/twkm.R
wskm/R/levelplot.ewkm.R
wskm/MD5
wskm/DESCRIPTION
wskm/man
wskm/man/ewkm.Rd
wskm/man/fgkm.sample.Rd
wskm/man/twkm.Rd
wskm/man/plot.ewkm.Rd
wskm/man/predict.ewkm.Rd
wskm/man/twkm.sample.Rd
wskm/man/fgkm.Rd