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

This package provides comprehensive implementations of algorithms based on rough set theory (RST) and fuzzy rough set theory (FRST), and integrates these two theories into a single package. It provides implementations, not only for the basic concepts of RST and FRST, but also most common methods based on them for handling some: discretization, feature selection, instance selection, rule induction, and classification based on nearest neighbors. RST was introduced by Zdzislaw Pawlak in 1982 as a sophisticated mathematical tool based on indiscernibility relations to model and process imprecise or incomplete information. It works on symbolic-valued datasets for tackling the data analysis problems. 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. In addition, we provide a new feature in this version which is missing value completion. Finally, our package should be considered as an alternative software library for analyzing data based on RST and FRST. Furthermore, in this version we provide some algorithms for dealing with missing values.

Version: 1.1-0
Suggests: sets, class
Published: 2014-06-19
Author: Lala Septem Riza, Andrzej Janusz, Chris Cornelis, Francisco Herrera, Dominik Slezak, and Jose Manuel Benitez
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_1.1-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: RoughSets_1.1-0.tgz, r-oldrel: RoughSets_1.1-0.tgz
OS X Mavericks binaries: r-release: RoughSets_1.1-0.tgz
Old sources: RoughSets archive