R: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

# Data Analysis Using Rough Set and Fuzzy Rough Set Theories

## Documentation for package ‘RoughSets’ version 1.3-7

- DESCRIPTION file.
- Code demos. Use demo() to run them.

## Help Pages

A.Introduction-RoughSets-package | Introduction to Rough Set Theory
B.Introduction-FuzzyRoughSets-package | Introduction to Fuzzy Rough Set Theory
RoughSets-package | Getting started with the RoughSets package
A.Introduction-RoughSets | 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
B.Introduction-FuzzyRoughSets | Introduction to Fuzzy Rough Set Theory
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
FuzzyRoughSets-intro | Introduction to Fuzzy Rough Set Theory
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
RoughSets-intro | Introduction to Rough Set Theory
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