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.

Install the latest version of this package by entering the following in R:

`install.packages("wskm")`

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 |

https://github.com/SimonYansenZhao/wskm, http://english.siat.cas.cn/ |

**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.

inst

inst/CITATION

inst/ChangeLog

tests

tests/Makefile

tests/fgkm_test.R
tests/twkm_test.Rout.save

tests/fgkm_test.Rout.save

tests/twkm_test.R
tests/ewkm01.Rout.save

tests/ewkm01.R
tests/ewkm01.csv

src

src/Utils.h

src/fgkm.c

src/twkm.c

src/Utils.c

src/ewkm.c

NAMESPACE

data

data/multiplefeatures_remove_pix.RData

data/datalist

data/fgkm.sample.RData

R

R/predict.ewkm.R
R/ewkm.R
R/plot.ewkm.R
R/fgkm.R
R/twkm.R
R/levelplot.ewkm.R
MD5

DESCRIPTION

man

man/ewkm.Rd
man/fgkm.sample.Rd
man/twkm.Rd
man/plot.ewkm.Rd
man/predict.ewkm.Rd
man/twkm.sample.Rd
man/fgkm.Rd
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