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.


Reference manual

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1.3-7 by Christoph Bergmeir, 2 years ago

Browse source code at

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]

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

GPL (>= 2) license

Depends on Rcpp

Suggests class

Linking to Rcpp

See at CRAN