Package: FPDC
Type: Package
Title: PD-clustering and factor PD-clustering
Version: 1.0
Date: 2013-11-05
Author: Cristina Tortora and Paul D. McNicholas
Maintainer: Cristina Tortora <ctortora@uoguelph.ca>
Description: 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.
Depends: ThreeWay
License: GPL (>= 2)
Packaged: 2014-02-26 15:43:24 UTC; cristina
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-02-26 17:05:00
