RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.

Version: 1.3-8
Depends: Rcpp
LinkingTo: Rcpp
Suggests: class
Published: 2024-01-23
DOI: 10.32614/CRAN.package.RoughSets
Author: Andrzej Janusz [aut], Lala Septem Riza [aut], Dominik Ślęzak [ctb], Chris Cornelis [ctb], Francisco Herrera [ctb], Jose Manuel Benitez [ctb], Christoph Bergmeir [ctb, cre], Sebastian Stawicki [ctb]
Maintainer: Christoph Bergmeir <c.bergmeir at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
In views: MachineLearning
CRAN checks: RoughSets results


Reference manual: RoughSets.pdf


Package source: RoughSets_1.3-8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): RoughSets_1.3-8.tgz, r-oldrel (arm64): RoughSets_1.3-8.tgz, r-release (x86_64): RoughSets_1.3-8.tgz, r-oldrel (x86_64): RoughSets_1.3-8.tgz
Old sources: RoughSets archive


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