FPDclustering: PD-Clustering and Factor PD-Clustering

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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.

Author
Cristina Tortora and Paul D. McNicholas
Date of publication
2016-05-18 01:23:36
Maintainer
Cristina Tortora <grikris1@gmail.com>
License
GPL (>= 2)
Version
1.1

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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

Files in this package

FPDclustering
FPDclustering/NAMESPACE
FPDclustering/data
FPDclustering/data/asymmetric20.rda
FPDclustering/data/asymmetric3.rda
FPDclustering/data/datalist
FPDclustering/data/outliers.rda
FPDclustering/R
FPDclustering/R/PDclust.R
FPDclustering/R/FPDC-internal.R
FPDclustering/R/Silh.R
FPDclustering/R/TuckerFactors.R
FPDclustering/R/disS.R
FPDclustering/R/FPDC.R
FPDclustering/MD5
FPDclustering/DESCRIPTION
FPDclustering/man
FPDclustering/man/asymmetric3.Rd
FPDclustering/man/PDclust.Rd
FPDclustering/man/FPDC.Rd
FPDclustering/man/TuckerFactors.Rd
FPDclustering/man/asymmetric20.Rd
FPDclustering/man/Silh.Rd
FPDclustering/man/outliers.Rd