wskm - Weighted k-Means Clustering
Entropy weighted k-means (ewkm) by Liping Jing, Michael K.
Ng and Joshua Zhexue Huang (2007) <doi:10.1109/TKDE.2007.1048>
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) by Xiaojun Chen, Xiaofei Xu, Joshua Zhexue
Huang and Yunming Ye (2013) <doi:10.1109/TKDE.2011.262>
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) by Xiaojun Chen, Yunminng Ye, Xiaofei
Xu and Joshua Zhexue Huang (2012)
<doi:10.1016/j.patcog.2011.06.004> extends this concept by
grouping features and weighting the group in addition to
weighting individual features.