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. GPDC and TPDC uses a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional data sets.

Getting started

Package details

AuthorCristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], Tina Kalra [aut], and Paul D. McNicholas [fnd]
MaintainerCristina Tortora <grikris1@gmail.com>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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FPDclustering documentation built on Aug. 31, 2022, 5:09 p.m.