Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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Documentation for package ‘RoughSets’ version 1.3-8

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RoughSets-package Getting started with the RoughSets package
as.character.RuleSetRST The 'as.character' method for RST rule sets
as.list.RuleSetRST The 'as.list' method for RST rule sets
BC.boundary.reg.RST Computation of a boundary region
BC.discernibility.mat.FRST The decision-relative discernibility matrix based on fuzzy rough set theory
BC.discernibility.mat.RST Computation of a decision-relative discernibility matrix based on the rough set theory
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 set theory
BC.LU.approximation.FRST The fuzzy lower and upper approximations based on fuzzy rough set theory
BC.LU.approximation.RST Computation of lower and upper approximations of decision classes
BC.negative.reg.RST Computation of a negative region
BC.positive.reg.FRST Positive region based on fuzzy rough set
BC.positive.reg.RST Computation of a positive region
C.FRNN.FRST The fuzzy-rough nearest neighbor algorithm
C.FRNN.O.FRST The fuzzy-rough ownership nearest neighbor algorithm
C.POSNN.FRST The positive region based fuzzy-rough nearest neighbor algorithm
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 quantile-based discretization
D.global.discernibility.heuristic.RST Supervised discretization based on the maximum discernibility heuristic
D.local.discernibility.heuristic.RST Supervised discretization based on the local discernibility heuristic
Extract.RuleSetRST The '[.' method for '"RuleSetRST"' objects
FS.all.reducts.computation A function for computing all decision reducts of a decision system
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 and approximate decision reducts
FS.greedy.heuristic.superreduct.RST The greedy heuristic method for determining superreduct based on RST
FS.nearOpt.fvprs.FRST The near-optimal reduction algorithm based on fuzzy rough set theory
FS.one.reduct.computation Computing one reduct from a discernibility matrix
FS.permutation.heuristic.reduct.RST The permutation heuristic algorithm for computation of a decision reduct
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
Introduction-FuzzyRoughSets Getting started with the RoughSets package
Introduction-RoughSets Getting started with the RoughSets package
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 common values
MV.mostCommonValResConcept The most common value or mean of an attribute restricted to a concept
predict.FRST The predicting function for rule induction methods based on FRST
predict.RST Prediction of decision classes using rule-based classifiers.
predict.RuleSetFRST The predicting function for rule induction methods based on FRST
predict.RuleSetRST Prediction of decision classes using rule-based 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.confidence Quality indicators of RST decision rules
RI.GFRS.FRST Generalized fuzzy rough set rule induction based on FRST
RI.hybridFS.FRST Hybrid fuzzy-rough 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
RI.lift Quality indicators of RST decision rules
RI.support Quality indicators of RST decision rules
RoughSetData Data set of the package
RoughSets 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 object
SF.read.DecisionTable Reading tabular data from files.
summary.IndiscernibilityRelation The summary function for an indiscernibility relation
summary.LowerUpperApproximation The summary function of lower and upper approximations based on RST and FRST
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 gini-index 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
[.RuleSetRST The '[.' method for '"RuleSetRST"' objects