Package: FPDclustering
Type: Package
Title: PD-Clustering and Factor PD-Clustering
Version: 1.4.1
Date: 2020-01-28
Author: Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], and Paul D. McNicholas [fnd]
Maintainer: Cristina Tortora <grikris1@gmail.com>
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. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses a dissimilarity measure based on densities. 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.
Depends: ThreeWay ,mvtnorm,R (>= 3.5)
Imports: ExPosition,cluster,rootSolve
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2020-01-28 18:15:17 UTC; cristina
Repository: CRAN
Date/Publication: 2020-01-28 20:50:05 UTC
Built: R 3.6.3; ; 2020-08-05 02:23:54 UTC; windows
