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.
Author  Lala Septem Riza [aut], Andrzej Janusz [aut], Dominik Ślęzak [ctb], Chris Cornelis [ctb], Francisco Herrera [ctb], Jose Manuel Benitez [ctb], Christoph Bergmeir [ctb, cre], Sebastian Stawicki [ctb] 
Date of publication  20150905 09:37:46 
Maintainer  Christoph Bergmeir <c.bergmeir@decsai.ugr.es> 
License  GPL (>= 2) 
Version  1.30 
URL  https://github.com/janusza/RoughSets 
Package repository  View on CRAN 
Installation  Install the latest version of this package by entering the following in R:



All man pages Function index File listing
Man pages  

A.IntroductionRoughSets: Introduction to Rough Set Theory  
as.character.RuleSetRST: The 'as.character' method for RST rule sets  
as.list.RuleSetRST: The 'as.list' method for RST rule sets  
BC.discernibility.mat.FRST: The decisionrelative discernibility matrix based on fuzzy...  
BC.discernibility.mat.RST: Computation of a decisionrelative discernibility matrix...  
BC.IND.relation.FRST: The indiscernibility relation based on fuzzy rough set theory  
BC.IND.relation.RST: Computation of indiscernibility classes based on the rough...  
BC.LU.approximation.FRST: The fuzzy lower and upper approximations based on fuzzy rough...  
BC.LU.approximation.RST: Computation of lower and upper approximations of decision...  
BC.positive.reg.FRST: Positive region based on fuzzy rough set  
BC.positive.reg.RST: Computation of a positive region  
B.IntroductionFuzzyRoughSets: Introduction to Fuzzy Rough Set Theory  
C.FRNN.FRST: The fuzzyrough nearest neighbor algorithm  
C.FRNN.O.FRST: The fuzzyrough ownership nearest neighbor algorithm  
C.POSNN.FRST: The positive region based fuzzyrough nearest neighbor...  
D.discretization.RST: The wrapper function for discretization methods  
D.discretize.equal.intervals.RST: Unsupervised discretization into intervals of equal length.  
D.discretize.quantiles.RST: The quantilebased discretization  
D.global.discernibility.heuristic.RST: Supervised discretization based on the maximum discernibility...  
D.local.discernibility.heuristic.RST: Supervised discretization based on the local discernibility...  
FS.all.reducts.computation: A function for computing all decision reducts of a decision...  
FS.DAAR.heuristic.RST: The DAAR heuristic for computation of decision reducts  
FS.feature.subset.computation: The superreduct computation based on RST and FRST  
FS.greedy.heuristic.reduct.RST: The greedy heuristic algorithm for computing decision reducts...  
FS.greedy.heuristic.superreduct.RST: The greedy heuristic method for determining superreduct based...  
FS.nearOpt.fvprs.FRST: The nearoptimal reduction algorithm based on fuzzy rough set...  
FS.one.reduct.computation: Computing one reduct from a discernibility matrix  
FS.permutation.heuristic.reduct.RST: The permutation heuristic algorithm for computation of a...  
FS.quickreduct.FRST: The fuzzy QuickReduct algorithm based on FRST  
FS.quickreduct.RST: QuickReduct algorithm based on RST  
FS.reduct.computation: The reduct computation methods based on RST and FRST  
IS.FRIS.FRST: The fuzzy rough instance selection algorithm  
IS.FRPS.FRST: The fuzzy rough prototype selection method  
MV.conceptClosestFit: Concept Closest Fit  
MV.deletionCases: Missing value completion by deleting instances  
MV.globalClosestFit: Global Closest Fit  
MV.missingValueCompletion: Wrapper function of missing value completion  
MV.mostCommonVal: Replacing missing attribute values by the attribute mean or...  
MV.mostCommonValResConcept: The most common value or mean of an attribute restricted to a...  
predict.RuleSetFRST: The predicting function for rule induction methods based on...  
predict.RuleSetRST: Prediction of decision classes using rulebased classifiers.  
print.FeatureSubset: The print method of FeatureSubset objects  
print.RuleSetRST: The print function for RST rule sets  
RI.AQRules.RST: Rule induction using the AQ algorithm  
RI.CN2Rules.RST: Rule induction using a version of CN2 algorithm  
RI.GFRS.FRST: Generalized fuzzy rough set rule induction based on FRST  
RI.hybridFS.FRST: Hybrid fuzzyrough rule and induction and feature selection  
RI.indiscernibilityBasedRules.RST: Rule induction from indiscernibility classes.  
RI.laplace: Quality indicators of RST decision rules  
RI.LEM2Rules.RST: Rule induction using the LEM2 algorithm  
RoughSetData: Data set of the package  
RoughSetspackage: Getting started with the RoughSets package  
SF.applyDecTable: Apply for obtaining a new decision table  
SF.asDecisionTable: Converting a data.frame into a 'DecisionTable' object  
SF.asFeatureSubset: Converting custom attribute name sets into a FeatureSubset...  
SF.read.DecisionTable: Reading tabular data from files.  
sub.RuleSetRST: The '[.' method for '"RuleSetRST"' objects  
summary.IndiscernibilityRelation: The summary function for an indiscernibility relation  
summary.LowerUpperApproximation: The summary function of lower and upper approximations based...  
summary.PositiveRegion: The summary function of positive region based on RST and FRST  
summary.RuleSetFRST: The summary function of rules based on FRST  
summary.RuleSetRST: The summary function of rules based on RST  
X.entropy: The entropy measure  
X.gini: The giniindex measure  
X.laplace: Rule voting by the Laplace estimate  
X.nOfConflicts: The discernibility measure  
X.rulesCounting: Rule voting by counting matching rules  
X.ruleStrength: Rule voting by strength of the rule 
Functions 

Files  

src
 
src/indiscernibility.cpp
 
src/discretization.cc
 
src/RcppExports.cpp
 
NAMESPACE
 
demo
 
demo/FS.permutation.heuristic.reduct.RST.R  
demo/FRNN.O.iris.R  
demo/FS.QuickReduct.FRST.Ex3.R  
demo/RI.classification.FRST.R  
demo/FS.QuickReduct.FRST.Ex5.R  
demo/FRNN.iris.R  
demo/DiscernibilityMatrix.FRST.R  
demo/FS.QuickReduct.FRST.Ex4.R  
demo/IS.FRPS.FRST.R  
demo/GettingStarted.B.R  
demo/FS.greedy.heuristic.reduct.RST.R  
demo/POSNN.iris.R  
demo/SimulationDataAnalysisWine.R  
demo/FS.QuickReduct.FRST.Ex2.R  
demo/RI.indiscernibilityBasedRules.RST.R  
demo/FS.greedy.heuristic.superreduct.RST.R  
demo/BasicConcept.FRST.R  
demo/D.discretize.quantiles.RST.R  
demo/IS.FRIS.FRST.R  
demo/BasicConcept.RST.R  
demo/RI.regression.FRST.R  
demo/FS.QuickReduct.FRST.Ex1.R  
demo/00Index
 
demo/D.discretize.equal.intervals.RST.R  
demo/FS.nearOpt.fvprs.FRST.R  
demo/GettingStarted.A.R  
demo/MV.simpleData.R  
demo/D.global.discernibility.heuristic.RST.R  
demo/FS.QuickReduct.RST.R  
demo/DiscernibilityMatrix.RST.R  
data
 
data/RoughSetData.RData
 
R
 
R/BasicFuzzyRoughSets.R  
R/InstanceSelection.R  
R/BasicRoughSets.OtherFuncCollections.R  
R/Discretization.R  
R/InstanceSelection.OtherFuncCollections.R  
R/IOFunctions.R  
R/FeatureSelection.R  
R/MissingValue.R  
R/FuzzyRoughSetsintroduction.R  
R/RoughSetspackage.R  
R/RuleInduction.OtherFuncCollections.R  
R/FeatureSelection.OtherFuncCollections.R  
R/RcppExports.R  
R/Discretization.OtherFuncCollections.R  
R/docData.R  
R/RuleInduction.R  
R/NearestNeigbour.OtherFuncCollections.R  
R/BasicRoughSets.R  
R/NearestNeigbour.R  
R/RoughSetsintroduction.R  
MD5
 
DESCRIPTION
 
man
 
man/print.RuleSetRST.Rd  
man/X.rulesCounting.Rd  
man/X.ruleStrength.Rd  
man/FS.feature.subset.computation.Rd  
man/predict.RuleSetRST.Rd  
man/A.IntroductionRoughSets.Rd  
man/BC.positive.reg.RST.Rd  
man/IS.FRIS.FRST.Rd  
man/FS.permutation.heuristic.reduct.RST.Rd  
man/C.POSNN.FRST.Rd  
man/SF.read.DecisionTable.Rd  
man/D.discretize.equal.intervals.RST.Rd  
man/RI.CN2Rules.RST.Rd  
man/RI.indiscernibilityBasedRules.RST.Rd  
man/RI.GFRS.FRST.Rd  
man/predict.RuleSetFRST.Rd  
man/RI.laplace.Rd  
man/MV.missingValueCompletion.Rd  
man/SF.asFeatureSubset.Rd  
man/BC.discernibility.mat.RST.Rd  
man/MV.globalClosestFit.Rd  
man/C.FRNN.FRST.Rd  
man/C.FRNN.O.FRST.Rd  
man/B.IntroductionFuzzyRoughSets.Rd  
man/IS.FRPS.FRST.Rd  
man/summary.RuleSetFRST.Rd  
man/MV.mostCommonVal.Rd  
man/print.FeatureSubset.Rd  
man/BC.IND.relation.FRST.Rd  
man/RoughSetData.Rd  
man/FS.one.reduct.computation.Rd  
man/as.list.RuleSetRST.Rd  
man/BC.LU.approximation.FRST.Rd  
man/FS.greedy.heuristic.reduct.RST.Rd  
man/MV.conceptClosestFit.Rd  
man/summary.PositiveRegion.Rd  
man/BC.discernibility.mat.FRST.Rd  
man/BC.IND.relation.RST.Rd  
man/RI.hybridFS.FRST.Rd  
man/RoughSetspackage.Rd  
man/FS.all.reducts.computation.Rd  
man/MV.deletionCases.Rd  
man/FS.greedy.heuristic.superreduct.RST.Rd  
man/X.gini.Rd  
man/FS.quickreduct.RST.Rd  
man/D.local.discernibility.heuristic.RST.Rd  
man/X.entropy.Rd  
man/FS.DAAR.heuristic.RST.Rd  
man/summary.RuleSetRST.Rd  
man/BC.positive.reg.FRST.Rd  
man/D.discretization.RST.Rd  
man/D.discretize.quantiles.RST.Rd  
man/summary.IndiscernibilityRelation.Rd  
man/MV.mostCommonValResConcept.Rd  
man/FS.reduct.computation.Rd  
man/FS.quickreduct.FRST.Rd  
man/SF.asDecisionTable.Rd  
man/D.global.discernibility.heuristic.RST.Rd  
man/X.nOfConflicts.Rd  
man/RI.AQRules.RST.Rd  
man/summary.LowerUpperApproximation.Rd  
man/X.laplace.Rd  
man/RI.LEM2Rules.RST.Rd  
man/SF.applyDecTable.Rd  
man/BC.LU.approximation.RST.Rd  
man/as.character.RuleSetRST.Rd  
man/sub.RuleSetRST.Rd  
man/FS.nearOpt.fvprs.FRST.Rd 
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