FPDclustering: PD-Clustering and Factor PD-Clustering
Version 1.1

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.

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AuthorCristina Tortora and Paul D. McNicholas
Date of publication2016-05-18 01:23:36
MaintainerCristina Tortora <grikris1@gmail.com>
LicenseGPL (>= 2)
Version1.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("FPDclustering")

Man pages

asymmetric20: Asymmetric data set shape=20
asymmetric3: Asymmetric data set shape=3
FPDC: Factor probabilistic distance clustering
outliers: Data set with outliers
PDclust: Probabilistic Distance Clustering
Silh: Probabilistic silhouette plot
TuckerFactors: Choice of the number of Tucker 3 factors

Functions

FPDC Man page Source code
PDclust Man page Source code
Silh Man page Source code
TuckerFactors Man page Source code
asymmetric20 Man page
asymmetric3 Man page
disS Source code
outliers Man page

Files

NAMESPACE
data
data/asymmetric20.rda
data/asymmetric3.rda
data/datalist
data/outliers.rda
R
R/PDclust.R
R/FPDC-internal.R
R/Silh.R
R/TuckerFactors.R
R/disS.R
R/FPDC.R
MD5
DESCRIPTION
man
man/asymmetric3.Rd
man/PDclust.Rd
man/FPDC.Rd
man/TuckerFactors.Rd
man/asymmetric20.Rd
man/Silh.Rd
man/outliers.Rd
FPDclustering documentation built on May 29, 2017, 3:50 p.m.