whiboclustering: White Box Clustering Algorithm Design

White Box Cluster Algorithm Design allows you to create Representative based cluster algorithm by using reusable components. This way one can recreate already available cluster algorithms (i.e. K-Means, K-Means++, PAM) but also create new cluster algorithms not available in the literature or any other software. For more information see papers <doi:10.1007/s10462-009-9133-6> and <doi:10.1016/j.datak.2012.03.005>.

Version: 0.1.2
Depends: graphics, stats, clusterCrit, cluster
Suggests: methods, testthat
Published: 2018-11-20
Author: Sandro Radovanovic, Milan Vukicevic
Maintainer: Sandro Radovanovic <sandro.radovanovic at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: whiboclustering results


Reference manual: whiboclustering.pdf
Package source: whiboclustering_0.1.2.tar.gz
Windows binaries: r-devel: whiboclustering_0.1.2.zip, r-release: whiboclustering_0.1.2.zip, r-oldrel: whiboclustering_0.1.2.zip
OS X binaries: r-release: whiboclustering_0.1.2.tgz, r-oldrel: whiboclustering_0.1.2.tgz


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