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:
wskm_1.4.40.tar.gz
wskm_1.4.40.tar.gz(r-4.5-noble)wskm_1.4.40.tar.gz(r-4.4-noble)
wskm_1.4.40.tgz(r-4.4-emscripten)wskm_1.4.40.tgz(r-4.3-emscripten)
wskm.pdf |wskm.html✨
wskm/json (API)
# Install 'wskm' in R: |
install.packages('wskm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/simonyansenzhao/wskm/issues
- fgkm.sample - Sample dataset to illustrate the fgkm algorithm.
- twkm.sample - Sample dataset to test the twkm algorithm.
Last updated 5 years agofrom:881bfb4787. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 23 2024 |
R-4.5-linux-x86_64 | OK | Dec 23 2024 |
Dependencies:classclusterdeldirDEoptimRdiptestflexmixfpcinterpjpegkernlablatticelatticeExtraMASSmclustmodeltoolsnnetpngprabclusRColorBrewerRcppRcppEigenrobustbase
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Entropy Weighted K-Means | ewkm |
Feature Group Weighting K-Means for Subspace clustering | fgkm |
Sample dataset to illustrate the fgkm algorithm. | fgkm.sample |
Plot Entropy Weighted K-Means Weights | levelplot.ewkm plot.ewkm |
Predict method for 'ewkm' model. | predict predict.ewkm |
Two-level variable weighting clustering | twkm |
Sample dataset to test the twkm algorithm. | twkm.sample |