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
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-release (x86_64): FairMclus_2.2.1.tgz, r-oldrel: FairMclus_2.2.1.tgz

Linking:

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