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: |
2.3.2 |
| Depends: |
ThreeWay, mvtnorm, R (≥ 3.5) |
| Imports: |
ExPosition, cluster, rootSolve, MASS, klaR, GGally, ggplot2, ggeasy |
| Published: |
2024-12-12 |
| 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 |
| CRAN checks: |
FPDclustering results |
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