CRAN/E | RoughSets

RoughSets

Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Installation

About

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.

github.com/janusza/RoughSets

Key Metrics

Version 1.3-8
Published 2024-01-23 146 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks RoughSets results

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Maintainer

Maintainer

Christoph Bergmeir

Authors

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

Material

Reference manual
Package source

In Views

MachineLearning

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

RoughSets archive

Depends

Rcpp

Suggests

class

LinkingTo

Rcpp