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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=FPDclustering to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4