Probabilistic distance clustering (PDclustering) is an iterative, distribution free, probabilistic clustering method. PDclustering 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. PDclustering is a flexible method that can be used with nonspherical 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 PDclustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PDclustering criterion. It works on high dimensional data sets.
Package details 


Author  Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], Tina Kalra [aut], and Paul D. McNicholas [fnd] 
Maintainer  Cristina Tortora <grikris1@gmail.com> 
License  GPL (>= 2) 
Version  2.2 
Package repository  View on CRAN 
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