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.
