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FPDclustering
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Download and install FPDclustering package within the R console
Install from CRAN:
install.packages("FPDclustering")
Install from Github:
library("remotes")
install_github("cran/FPDclustering")
Install by package version:
library("remotes")
install_version("FPDclustering", "2.3.1")
Attach the package and use:
library("FPDclustering")
Maintained by
Cristina Tortora
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-12-17
Latest Update: 2022-08-31
Description:
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.
How to cite:
Cristina Tortora (2014). FPDclustering: PD-Clustering and Related Methods. R package version 2.3.1, https://cran.r-project.org/web/packages/FPDclustering
Previous versions and publish date:
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Other R packages that FPDclustering depends,
imports, suggests or enhances
Functions, R codes and Examples using
the FPDclustering R package
Some associated functions: FPDC . GPDC . PDC . PDQ . Silh . Star . TPDC . TuckerFactors . ais . asymmetric20 . asymmetric3 . outliers . plot.FPDclustering . summary.FPDclustering .
Some associated R codes: FPDC-internal.R . FPDC.R . GPDC.R . PDC.R . PDQ.R . Silh.R . TPDC.R . TuckerFactors.R . disS.R . Full FPDclustering package functions and examples
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