R package wskm: Weighted k-Means Clustering. 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.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
- Kampert, Maarten M.; Meulman, Jacqueline J.; Friedman, Jerome H.: rCOSA: a software package for clustering objects on subsets of attributes (2017)