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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.
| Version: | 2.3.1 |
| Depends: | ThreeWay , mvtnorm, R (≥ 3.5) |
| Imports: | ExPosition, cluster, rootSolve, MASS, klaR, GGally, ggplot2, ggeasy |
| Published: | 2024-01-30 |
| DOI: | 10.32614/CRAN.package.FPDclustering |
| Author: | Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], Tina Kalra [aut], and Paul D. McNicholas [fnd] |
| Maintainer: | Cristina Tortora <grikris1 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | FPDclustering results |
| Reference manual: | FPDclustering.pdf |
| Package source: | FPDclustering_2.3.1.tar.gz |
| Windows binaries: | r-devel: FPDclustering_2.3.1.zip, r-release: FPDclustering_2.3.1.zip, r-oldrel: FPDclustering_2.3.1.zip |
| macOS binaries: | r-release (arm64): FPDclustering_2.3.1.tgz, r-oldrel (arm64): FPDclustering_2.3.1.tgz, r-release (x86_64): FPDclustering_2.3.1.tgz, r-oldrel (x86_64): FPDclustering_2.3.1.tgz |
| Old sources: | FPDclustering archive |
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