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. PDQ is an extension of the algorithm for clusters of different size. Factor 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: 1.3.1
Depends: ThreeWay , mvtnorm, R (≥ 3.5)
Imports: ExPosition, cluster
Published: 2019-08-22
Author: Cristina Tortora [aut, cre, cph], Noe Vidales [aut], and Paul D. McNicholas [fnd]
Maintainer: Cristina Tortora <grikris1 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: FPDclustering results

Downloads:

Reference manual: FPDclustering.pdf
Package source: FPDclustering_1.3.1.tar.gz
Windows binaries: r-devel: FPDclustering_1.3.zip, r-release: FPDclustering_1.3.zip, r-oldrel: FPDclustering_1.3.zip
OS X binaries: r-release: FPDclustering_1.3.tgz, r-oldrel: FPDclustering_1.3.tgz
Old sources: FPDclustering archive

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