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wskm
View on CRAN: Click
here
Download and install wskm package within the R console
Install from CRAN:
install.packages("wskm")
Install from Github:
library("remotes")
install_github("cran/wskm")
Install by package version:
library("remotes")
install_version("wskm", "1.4.40")
Attach the package and use:
library("wskm")
Maintained by
He Zhao
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-05-29
Latest Update: 2020-04-05
Description:
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.
How to cite:
He Zhao (2014). wskm: Weighted k-Means Clustering. R package version 1.4.40, https://cran.r-project.org/web/packages/wskm. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for wskm
Functions, R codes and Examples using
the wskm R package
Some associated functions: ewkm . fgkm . fgkm.sample . plot.ewkm . predict.ewkm . twkm . twkm.sample .
Some associated R codes: ewkm.R . fgkm.R . levelplot.ewkm.R . plot.ewkm.R . predict.ewkm.R . twkm.R . Full wskm package functions and examples
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