FPDclustering: PD-Clustering and Factor PD-Clustering

Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.

Version: 1.2
Depends: ThreeWay
Published: 2017-08-23
Author: Cristina Tortora and Paul D. McNicholas
Maintainer: Cristina Tortora <grikris1 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: FPDclustering results


Reference manual: FPDclustering.pdf
Package source: FPDclustering_1.2.tar.gz
Windows binaries: r-devel: FPDclustering_1.2.zip, r-release: FPDclustering_1.2.zip, r-oldrel: FPDclustering_1.2.zip
OS X El Capitan binaries: r-release: FPDclustering_1.2.tgz
OS X Mavericks binaries: r-oldrel: FPDclustering_1.2.tgz
Old sources: FPDclustering archive


Please use the canonical form https://CRAN.R-project.org/package=FPDclustering to link to this page.