Package: RoughSets 1.3-7.1

Christoph Bergmeir

RoughSets: 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.

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]

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RoughSets/json (API)

# Install 'RoughSets' in R:
install.packages('RoughSets', repos = c('https://janusza.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/janusza/roughsets/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

58 exports 35 stars 2.90 score 1 dependencies 2 mentions 183 scripts 539 downloads

Last updated 5 years agofrom:e541657d5b. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 04 2024
R-4.5-win-x86_64NOTESep 04 2024
R-4.5-linux-x86_64NOTESep 04 2024
R-4.4-win-x86_64NOTESep 04 2024
R-4.4-mac-x86_64NOTESep 04 2024
R-4.4-mac-aarch64NOTESep 04 2024
R-4.3-win-x86_64OKSep 04 2024
R-4.3-mac-x86_64OKSep 04 2024
R-4.3-mac-aarch64OKSep 04 2024

Exports:BC.boundary.reg.RSTBC.discernibility.mat.FRSTBC.discernibility.mat.RSTBC.discernibility.positive.mat.RSTBC.IND.relation.FRSTBC.IND.relation.RSTBC.LU.approximation.FRSTBC.LU.approximation.RSTBC.negative.reg.RSTBC.positive.reg.FRSTBC.positive.reg.RSTC.FRNN.FRSTC.FRNN.O.FRSTC.POSNN.FRSTD.discretization.RSTD.discretize.equal.intervals.RSTD.discretize.quantiles.RSTD.global.discernibility.heuristic.RSTD.local.discernibility.heuristic.RSTFS.all.reducts.computationFS.DAAR.heuristic.RSTFS.feature.subset.computationFS.greedy.heuristic.reduct.RSTFS.greedy.heuristic.superreduct.RSTFS.nearOpt.fvprs.FRSTFS.one.reduct.computationFS.permutation.heuristic.reduct.RSTFS.quickreduct.FRSTFS.quickreduct.RSTFS.reduct.computationIS.FRIS.FRSTIS.FRPS.FRSTMV.conceptClosestFitMV.deletionCasesMV.globalClosestFitMV.missingValueCompletionMV.mostCommonValMV.mostCommonValResConceptRI.AQRules.RSTRI.CN2Rules.RSTRI.confidenceRI.GFRS.FRSTRI.hybridFS.FRSTRI.indiscernibilityBasedRules.RSTRI.laplaceRI.LEM2Rules.RSTRI.liftRI.supportSF.applyDecTableSF.asDecisionTableSF.asFeatureSubsetSF.read.DecisionTableX.entropyX.giniX.laplaceX.nOfConflictsX.rulesCountingX.ruleStrength

Dependencies:Rcpp

Readme and manuals

Help Manual

Help pageTopics
Getting started with the RoughSets packageRoughSets-package RoughSets
The '[.' method for '"RuleSetRST"' objectsExtract.RuleSetRST [.RuleSetRST
Introduction to Rough Set TheoryA.Introduction-RoughSets RoughSets-intro
The 'as.character' method for RST rule setsas.character.RuleSetRST
The 'as.list' method for RST rule setsas.list.RuleSetRST
Introduction to Fuzzy Rough Set TheoryB.Introduction-FuzzyRoughSets FuzzyRoughSets-intro
Computation of a boundary regionBC.boundary.reg.RST
The decision-relative discernibility matrix based on fuzzy rough set theoryBC.discernibility.mat.FRST
Computation of a decision-relative discernibility matrix based on the rough set theoryBC.discernibility.mat.RST
Computation of a positive decision-relative discernibility matrix based on the rough set theoryBC.discernibility.positive.mat.RST
The indiscernibility relation based on fuzzy rough set theoryBC.IND.relation.FRST
Computation of indiscernibility classes based on the rough set theoryBC.IND.relation.RST
The fuzzy lower and upper approximations based on fuzzy rough set theoryBC.LU.approximation.FRST
Computation of lower and upper approximations of decision classesBC.LU.approximation.RST
Computation of a negative regionBC.negative.reg.RST
Positive region based on fuzzy rough setBC.positive.reg.FRST
Computation of a positive regionBC.positive.reg.RST
The fuzzy-rough nearest neighbor algorithmC.FRNN.FRST
The fuzzy-rough ownership nearest neighbor algorithmC.FRNN.O.FRST
The positive region based fuzzy-rough nearest neighbor algorithmC.POSNN.FRST
The wrapper function for discretization methodsD.discretization.RST
Unsupervised discretization into intervals of equal length.D.discretize.equal.intervals.RST
The quantile-based discretizationD.discretize.quantiles.RST
Supervised discretization based on the maximum discernibility heuristicD.global.discernibility.heuristic.RST
Supervised discretization based on the local discernibility heuristicD.local.discernibility.heuristic.RST
A function for computing all decision reducts of a decision systemFS.all.reducts.computation
The DAAR heuristic for computation of decision reductsFS.DAAR.heuristic.RST
The superreduct computation based on RST and FRSTFS.feature.subset.computation
The greedy heuristic algorithm for computing decision reducts and approximate decision reductsFS.greedy.heuristic.reduct.RST
The greedy heuristic method for determining superreduct based on RSTFS.greedy.heuristic.superreduct.RST
The near-optimal reduction algorithm based on fuzzy rough set theoryFS.nearOpt.fvprs.FRST
Computing one reduct from a discernibility matrixFS.one.reduct.computation
The permutation heuristic algorithm for computation of a decision reductFS.permutation.heuristic.reduct.RST
The fuzzy QuickReduct algorithm based on FRSTFS.quickreduct.FRST
QuickReduct algorithm based on RSTFS.quickreduct.RST
The reduct computation methods based on RST and FRSTFS.reduct.computation
The fuzzy rough instance selection algorithmIS.FRIS.FRST
The fuzzy rough prototype selection methodIS.FRPS.FRST
Concept Closest FitMV.conceptClosestFit
Missing value completion by deleting instancesMV.deletionCases
Global Closest FitMV.globalClosestFit
Wrapper function of missing value completionMV.missingValueCompletion
Replacing missing attribute values by the attribute mean or common valuesMV.mostCommonVal
The most common value or mean of an attribute restricted to a conceptMV.mostCommonValResConcept
The predicting function for rule induction methods based on FRSTpredict.FRST predict.RuleSetFRST
Prediction of decision classes using rule-based classifiers.predict.RST predict.RuleSetRST
The print method of FeatureSubset objectsprint.FeatureSubset
The print function for RST rule setsprint.RuleSetRST
Rule induction using the AQ algorithmRI.AQRules.RST
Rule induction using a version of CN2 algorithmRI.CN2Rules.RST
Generalized fuzzy rough set rule induction based on FRSTRI.GFRS.FRST
Hybrid fuzzy-rough rule and induction and feature selectionRI.hybridFS.FRST
Rule induction from indiscernibility classes.RI.indiscernibilityBasedRules.RST
Quality indicators of RST decision rulesRI.confidence RI.laplace RI.lift RI.support
Rule induction using the LEM2 algorithmRI.LEM2Rules.RST
Data set of the packageRoughSetData
Apply for obtaining a new decision tableSF.applyDecTable
Converting a data.frame into a 'DecisionTable' objectSF.asDecisionTable
Converting custom attribute name sets into a FeatureSubset objectSF.asFeatureSubset
Reading tabular data from files.SF.read.DecisionTable
The summary function for an indiscernibility relationsummary.IndiscernibilityRelation
The summary function of lower and upper approximations based on RST and FRSTsummary.LowerUpperApproximation
The summary function of positive region based on RST and FRSTsummary.PositiveRegion
The summary function of rules based on FRSTsummary.RuleSetFRST
The summary function of rules based on RSTsummary.RuleSetRST
The entropy measureX.entropy
The gini-index measureX.gini
Rule voting by the Laplace estimateX.laplace
The discernibility measureX.nOfConflicts
Rule voting by counting matching rulesX.rulesCounting
Rule voting by strength of the ruleX.ruleStrength