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.


Reference manual

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2.0 by Cristina Tortora, 5 months ago

Browse source code at https://github.com/cran/FPDclustering

Authors: Cristina Tortora [aut, cre, cph] , Noe Vidales [aut] , Francesco Palumbo [aut] , Tina Kalra [aut] , and Paul D. McNicholas [fnd]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports ExPosition, cluster, rootSolve, MASS, klaR, GGally, ggplot2

Depends on ThreeWay, mvtnorm

See at CRAN