Package: RoughSets
Maintainer: Christoph Bergmeir <c.bergmeir@decsai.ugr.es>
License: GPL (>= 2)
Title: Data Analysis Using Rough Set and Fuzzy Rough Set Theories.
Author: Lala Septem Riza, Andrzej Janusz, Chris Cornelis, Francisco
Herrera, Dominik Slezak, and Jose Manuel Benitez
Description: 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
URL: http://sci2s.ugr.es/dicits/software/RoughSets
Date: 2014-02-04
Suggests: sets, class
Packaged: 2014-06-19 10:38:20 UTC; Lala
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-06-19 13:14:53

model and process imprecise or incomplete information. By using

Authors@R: c(person("Andrzej", "Janusz", role = "aut",
person("Lala Septem", "Riza", role = "aut",
Description: 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
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