FairMclus: Clustering for Data with Sensitive Attribute

Clustering for categorical and mixed-type of data, to preventing classification biases due to race, gender or others sensitive attributes. This algorithm is an extension of the methodology proposed by "Santos & Heras (2020) <doi:10.28945/4643>".

Version:	2.2.1
Imports:	dplyr, irr, rlist, tidyr, parallel, magrittr, cluster, base, data.table, foreach, doParallel
Published:	2021-11-19
DOI:	10.32614/CRAN.package.FairMclus
Author:	Carlos Santos-Mangudo [aut, cre]
Maintainer:	Carlos Santos-Mangudo <carlossantos.csm at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	FairMclus results

Documentation:

Reference manual:

FairMclus.pdf

Downloads:

Package source:	FairMclus_2.2.1.tar.gz
Windows binaries:	r-devel: FairMclus_2.2.1.zip, r-release: FairMclus_2.2.1.zip, r-oldrel: FairMclus_2.2.1.zip
macOS binaries:	r-release (arm64): FairMclus_2.2.1.tgz, r-oldrel (arm64): FairMclus_2.2.1.tgz, r-release (x86_64): FairMclus_2.2.1.tgz, r-oldrel (x86_64): FairMclus_2.2.1.tgz

Linking:

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