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)|
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.