FPDC: 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 allows clustering of high dimensional data sets.

Version: 1.0
Depends: ThreeWay
Published: 2014-02-26
Author: Cristina Tortora and Paul D. McNicholas
Maintainer: Cristina Tortora <ctortora at uoguelph.ca>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: FPDC results


Reference manual: FPDC.pdf
Package source: FPDC_1.0.tar.gz
Windows binaries: r-devel: FPDC_1.0.zip, r-release: FPDC_1.0.zip, r-oldrel: FPDC_1.0.zip
OS X Snow Leopard binaries: r-release: FPDC_1.0.tgz, r-oldrel: FPDC_1.0.tgz
OS X Mavericks binaries: r-release: FPDC_1.0.tgz