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RoughSets (version 1.2-1)

Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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 Zdzisław Pawlak in 1982 as a sophisticated mathematical tool based on indiscernibility relations to model and process imprecise or incomplete information. It works on symbolic-valued datasets for tackling the data analysis problems. 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.

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Version

Install

install.packages('RoughSets')

Monthly Downloads

354

Version

1.2-1

License

GPL (>= 2)

Maintainer

Christoph Bergmeir

Last Published

March 24th, 2015

Functions in RoughSets (1.2-1)

C.POSNN.FRST

The positive region based fuzzy-rough nearest neighbor algorithm
FS.permutation.heuristic.reduct.RST

The permutation heuristic algorithm for computation of a decision reduct
BC.IND.relation.FRST

The indiscernibility relation based on fuzzy rough set theory
MV.globalClosestFit

Global Closest Fit
D.max.discernibility.matrix.RST

The maximal discernibility algorithm
RI.indiscernibilityBasedRules.RST

Rule induction from indiscernibility classes.
FS.DAAR.heuristic.RST

The DAAR heuristic for computation of decision reducts
FS.greedy.heuristic.superreduct.RST

The greedy heuristic method for determining superreduct based on RST
C.FRNN.O.FRST

The fuzzy-rough ownership nearest neighbor algorithm
FS.reduct.computation

The reduct computation methods based on RST and FRST
MV.mostCommonValResConcept

The most common value or mean of an attribute restricted to a concept
FS.one.reduct.computation

Computing one reduct from a discernibility matrix
IS.FRIS.FRST

The fuzzy rough instance selection algorithm
BC.positive.reg.RST

Computation of a positive region
D.discretize.quantiles.RST

The quantile-based discretization
BC.LU.approximation.FRST

The fuzzy lower and upper approximations based on fuzzy rough set theory
MV.mostCommonVal

Replacing missing attribute values by the attribute mean or common values
FS.all.reducts.computation

A function for computing all decision reducts of a decision system
RI.AQRules.RST

Rule induction using the AQ algorithm
IS.FRPS.FRST

The fuzzy rough prototype selection method
RI.hybridFS.FRST

Hybrid fuzzy-rough rule and induction and feature selection
BC.LU.approximation.RST

Computation of lower and upper approximations of decision classes
X.laplace

Rule voting by the Laplace estimate
SF.asFeatureSubset

Converting custom attribute name sets into a FeatureSubset object
D.discretization.RST

The wrapper function for discretization methods
summary.RuleSetRST

The summary function of rules based on RST
summary.RuleSetFRST

The summary function of rules based on FRST
predict.RuleSetFRST

The predicting function for rule induction methods based on FRST
BC.discernibility.mat.FRST

The decision-relative discernibility matrix based on fuzzy rough set theory
print.FeatureSubset

The print method of FeatureSubset objects
MV.missingValueCompletion

Wrapper function of missing value completion
B.Introduction-FuzzyRoughSets

Introduction to Fuzzy Rough Set Theory
FS.greedy.heuristic.reduct.RST

The greedy heuristic algorithm for computing decision reducts and approximate decision reducts
X.ruleStrength

Rule voting by strength of the rule
BC.positive.reg.FRST

Positive region based on fuzzy rough set
RoughSets-package

Getting started with the RoughSets package
FS.quickreduct.RST

QuickReduct algorithm based on RST
D.global.discernibility.heuristic.RST

Supervised discretization based on the maximum discernibility heuristic
SF.applyDecTable

Apply for obtaining a new decision table
FS.feature.subset.computation

The superreduct computation based on RST and FRST
summary.PositiveRegion

The summary function of positive region based on RST and FRST
SF.read.DecisionTable

Reading tabular data from files.
print.RuleSetRST

The print function for RST rule sets
C.FRNN.FRST

The fuzzy-rough nearest neighbor algorithm
MV.deletionCases

Missing value completion by deleting instances
X.gini

The gini-index measure
X.nOfConflicts

The discernibility measure
BC.discernibility.mat.RST

Computation of a decision-relative discernibility matrix based on the rough set theory
RoughSetData

Data set of the package
A.Introduction-RoughSets

Introduction to Rough Set Theory
D.discretize.equal.intervals.RST

Unsupervised discretization into intervals of equal length.
D.local.discernibility.matrix.RST

The local discretization strategy algorithm
RI.LEM2Rules.RST

Rule induction using the LEM2 algorithm
MV.conceptClosestFit

Concept Closest Fit
FS.quickreduct.FRST

The fuzzy QuickReduct algorithm based on FRST
summary.IndiscernibilityRelation

The summary function of indiscernibility relation based on RST and FRST
summary.LowerUpperApproximation

The summary function of lower and upper approximations based on RST and FRST
BC.IND.relation.RST

Computation of indiscernibility classes based on the rough set theory
X.entropy

The entropy measure
predict.RuleSetRST

Prediction of decision classes using rule-based classifiers.
FS.nearOpt.fvprs.FRST

The near-optimal reduction algorithm based on fuzzy rough set theory
RI.CN2Rules.RST

Rule induction using a version of CN2 algorithm
RI.GFRS.FRST

Generalized fuzzy rough set rule induction based on FRST
SF.asDecisionTable

Converting a data.frame into a DecisionTable object
X.rulesCounting

Rule voting by counting matching rules