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]|
|Maintainer||He Zhao <[email protected]>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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