Package: kml3d
Type: Package
Title: K-Means for Joint Longitudinal Data
Version: 2.4.1
Date: 2016-02-02
Authors@R: c(person("Christophe","Genolini",role=c("cre","aut"),email="christophe.genolini@u-paris10.fr"),
person("Bruno","Falissard",role=c("ctb")),
person("Jean-Baptiste","Pingault",role=c("ctb")))
Description: An implementation of k-means specifically design
to cluster joint trajectories (longitudinal data on
several variable-trajectories).
Like 'kml', it provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
interface for choosing the 'best' number of clusters. In addition, the 3D graph
representing the mean joint-trajectories of each cluster can be exported through
LaTeX in a 3D dynamic rotating PDF graph.
License: GPL (>= 2)
LazyData: yes
URL: http:www.r-project.org
Collate: global.r distance3d.r clusterLongData3d.r kml3d.r
Depends: methods, clv, rgl, misc3d, longitudinalData (>= 2.4), kml (>= 2.4)
Encoding: latin1
NeedsCompilation: no
Packaged: 2016-02-16 14:46:24 UTC; Christophe
Author: Christophe Genolini [cre, aut],
Bruno Falissard [ctb],
Jean-Baptiste Pingault [ctb]
Maintainer: Christophe Genolini <christophe.genolini@u-paris10.fr>
Repository: CRAN
Date/Publication: 2016-02-16 16:15:26
Package: wskm
Version: 1.4.28
Date: 2015-07-08
Title: Weighted k-Means Clustering
Authors@R: c(person("Graham", "Williams", email="graham.williams@togaware.com", role="aut"),
person("Joshua Z", "Huang", email="zx.huang@szu.edu.cn", role="aut"),
person("Xiaojun", "Chen", email="xjchen.hitsz@gmail.com", role="aut"),
person("Qiang", "Wang", role="aut"),
person("Longfei", "Xiao", role="aut"),
person("He", "Zhao", email="Simon.Yansen.Zhao@gmail.com", role="cre"))
Maintainer: He Zhao <Simon.Yansen.Zhao@gmail.com>
Depends: R (>= 2.10), grDevices, stats, lattice, latticeExtra, clv
Description: 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.
License: GPL (>= 3)
Copyright: 2011-2014 Shenzhen Institutes of Advanced Technology Chinese
Academy of Sciences
LazyLoad: yes
LazyData: yes
URL: https://github.com/SimonYansenZhao/wskm,
http://english.siat.cas.cn/
BugReports: https://github.com/SimonYansenZhao/wskm/issues
NeedsCompilation: yes
Packaged: 2015-07-08 11:47:00 UTC; simon
Author: Graham Williams [aut],
Joshua Z Huang [aut],
Xiaojun Chen [aut],
Qiang Wang [aut],
Longfei Xiao [aut],
He Zhao [cre]
Repository: CRAN
Date/Publication: 2015-07-08 14:46:30
providing 
.