Package: wskm 1.4.40

He Zhao

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.

Authors:Graham Williams [aut], Joshua Z Huang [aut], Xiaojun Chen [aut], Qiang Wang [aut], Longfei Xiao [aut], He Zhao [cre]

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Bug tracker:https://github.com/simonyansenzhao/wskm/issues

3 exports 1 stars 0.00 score 22 dependencies 18 scripts 289 downloads

Last updated 4 years agofrom:881bfb4787. Checks:OK: 2. Indexed: yes.

Exports:ewkmfgkmtwkm

Dependencies:classclusterdeldirDEoptimRdiptestflexmixfpcinterpjpegkernlablatticelatticeExtraMASSmclustmodeltoolsnnetpngprabclusRColorBrewerRcppRcppEigenrobustbase