FPDclustering: PD-Clustering and Related Methods
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 center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use 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.
| Version: | 2.3.5 |
| Depends: | ThreeWay,mvtnorm, R (≥ 4.1.0) |
| Imports: | ExPosition,cluster,rootSolve,MASS,klaR,GGally,ggplot2,ggeasy |
| Published: | 2025-03-06 |
| DOI: | 10.32614/CRAN.package.FPDclustering |
| Author: | Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], Tina Kalra [aut], Paul D. McNicholas [fnd] |
| Maintainer: | Cristina Tortora <grikris1 at gmail.com> |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | FPDclustering citation info |
| In views: | Cluster |
| CRAN checks: | FPDclustering results |
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