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RoughSets
View on CRAN: Click
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Download and install RoughSets package within the R console
Install from CRAN:
install.packages("RoughSets")
Install from Github:
library("remotes")
install_github("cran/RoughSets")
Install by package version:
library("remotes")
install_version("RoughSets", "1.3-8")
Attach the package and use:
library("RoughSets")
Maintained by
Christoph Bergmeir
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-02-04
Latest Update: 2024-01-23
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
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 Zdzislaw
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.
How to cite:
Christoph Bergmeir (2014). RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories. R package version 1.3-8, https://cran.r-project.org/web/packages/RoughSets. Accessed 03 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for RoughSets
Functions, R codes and Examples using
the RoughSets R package
Some associated functions: A.Introduction-RoughSets . B.Introduction-FuzzyRoughSets . BC.IND.relation.FRST . BC.IND.relation.RST . BC.LU.approximation.FRST . BC.LU.approximation.RST . BC.boundary.reg.RST . BC.discernibility.mat.FRST . BC.discernibility.mat.RST . BC.negative.reg.RST . BC.positive.reg.FRST . BC.positive.reg.RST . C.FRNN.FRST . C.FRNN.O.FRST . C.POSNN.FRST . D.discretization.RST . D.discretize.equal.intervals.RST . D.discretize.quantiles.RST . D.global.discernibility.heuristic.RST . D.local.discernibility.heuristic.RST . FS.DAAR.heuristic.RST . FS.all.reducts.computation . FS.feature.subset.computation . FS.greedy.heuristic.reduct.RST . FS.greedy.heuristic.superreduct.RST . FS.nearOpt.fvprs.FRST . FS.one.reduct.computation . FS.permutation.heuristic.reduct.RST . FS.quickreduct.FRST . FS.quickreduct.RST . FS.reduct.computation . IS.FRIS.FRST . IS.FRPS.FRST . MV.conceptClosestFit . MV.deletionCases . MV.globalClosestFit . MV.missingValueCompletion . MV.mostCommonVal . MV.mostCommonValResConcept . RI.AQRules.RST . RI.CN2Rules.RST . RI.GFRS.FRST . RI.LEM2Rules.RST . RI.hybridFS.FRST . RI.indiscernibilityBasedRules.RST . RI.laplace . RoughSetData . RoughSets-package . SF.applyDecTable . SF.asDecisionTable . SF.asFeatureSubset . SF.read.DecisionTable . X.entropy . X.gini . X.laplace . X.nOfConflicts . X.ruleStrength . X.rulesCounting . as.character.RuleSetRST . as.list.RuleSetRST . predict.RuleSetFRST . predict.RuleSetRST . print.FeatureSubset . print.RuleSetRST . sub-.RuleSetRST . summary.IndiscernibilityRelation . summary.LowerUpperApproximation . summary.PositiveRegion . summary.RuleSetFRST . summary.RuleSetRST .
Some associated R codes: BasicFuzzyRoughSets.R . BasicRoughSets.OtherFuncCollections.R . BasicRoughSets.R . Discretization.OtherFuncCollections.R . Discretization.R . FeatureSelection.OtherFuncCollections.R . FeatureSelection.R . FuzzyRoughSets-introduction.R . IOFunctions.R . InstanceSelection.OtherFuncCollections.R . InstanceSelection.R . MissingValue.R . NearestNeigbour.OtherFuncCollections.R . NearestNeigbour.R . RcppExports.R . RoughSets-introduction.R . RoughSets-package.R . RuleInduction.OtherFuncCollections.R . RuleInduction.R . docData.R . Full RoughSets package functions and examples
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