Weighted k-Means Clustering
Description
Entropy weighted k-means (ewkm) by Liping Jing, Michael
K. Ng and Joshua Zhexue Huang (2007)
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)
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)
extends this concept by
grouping features and weighting the group in addition to
weighting individual features.