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RoughSets (version 1.0-0)

Data Analysis Using Rough Set and Fuzzy Rough Set Theories.

Description

This package provides comprehensive implementations of the rough set theory (RST) and the fuzzy rough set theory (FRST), and integrates these two theories into a single package. It provides implementations, not only for the basic concepts of RST and FRST, but also most common methods based on them for handling some tasks such as discretization, feature selection, instance selection, rule induction, and prediction. RST was introduced by Zdzislaw 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

1.0-0

License

GPL (>= 2)

Maintainer

Christoph Bergmeir

Last Published

February 4th, 2014

Functions in RoughSets (1.0-0)

D.global.discernibility.heuristic.RST

The global maximum discernibility heuristic
X.gini

The gini-index gain measure function
BC.discernibility.mat.FRST

The decision-relative discernibility matrix based on fuzzy rough set theory
A.Introduction-RoughSets

Introduction to Rough Set Theory
BC.LU.approximation.RST

The lower and upper approximation based on rough set
BC.discernibility.mat.RST

The decision-relative discernibility matrix based on rough set theory
D.local.discernibility.matrix.RST

The local strategy algorithm
FS.greedy.heuristic.reduct.RST

The greedy heuristic algorithm for determining a reduct
SF.read.DecisionTable

The importing function
BC.positive.reg.RST

Regions based on rough set theory
C.FRNN.O.FRST

The fuzzy-rough ownership nearest neighbour algorithm
BC.IND.relation.RST

Indiscernibility relation based on rough set theory
D.discretize.quantiles.RST

The "quantile-based" discretization algorithm
D.discretization.RST

The wrapper function of discretization methods
SF.applyDecTable

Apply for obtaining a new decision table
summary.IndiscernibilityRelation

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

the best-first voting strategy function
C.FRNN.FRST

The fuzzy-rough nearest neighbour algorithm
IS.FRPS.FRST

The fuzzy rough prototype selection method
BC.positive.reg.FRST

Positive region based on fuzzy rough set
RI.hybridFS.FRST

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

The fuzzy lower and upper approximation based on fuzzy rough set theory
X.entropy

The information gain measure function
D.discretize.equal.intervals.RST

The "equal interval size" discretization algorithm
FS.feature.subset.computation

The superreduct computation based on RST and FRST
FS.quickreduct.FRST

The fuzzy QuickReduct algorithm based on FRST
RoughSets-package

Getting started with the RoughSets package
FS.greedy.heuristic.superreduct.RST

The greedy heuristic method for determining superreduct based on RST
FS.permutation.heuristic.reduct.RST

The permutation heuristic algorithm for determining a reduct
predict.RuleSetFRST

The predicting function for rule induction methods based on FRST
FS.all.reducts.computation

The function for computing all reducts
FS.reduct.computation

The reduct computation methods based on RST and FRST
SF.asDecisionTable

The construction function
X.nOfConflictsLog

The discernibility measure function based on log2
RoughSetData

Data set of the package
IS.FRIS.FRST

The fuzzy rough instance selection algorithm
summary.PositiveRegion

The summary function of positive region based on RST and FRST
summary.RuleSetFRST

The summary function of rules based on FRST
RI.GFRS.FRST

Generalized fuzzy rough set rule induction based on FRST
FS.quickreduct.RST

QuickReduct algorithm based on RST
RI.indiscernibilityBasedRules.RST

Rule induction based on RST
C.POSNN.FRST

The positive region based fuzzy-rough nearest neighbour algorithm
BC.IND.relation.FRST

The indiscernibility relation based on fuzzy rough set theory
B.Introduction-FuzzyRoughSets

Introduction to Fuzzy Rough Set Theory
summary.LowerUpperApproximation

The summary function of lower and upper approximations based on RST and FRST
X.nOfConflicts

The discernibility measure function
predict.RuleSetRST

The predicting function for rule induction methods based on RST
X.nOfConflictsSqrt

The discernibility measure function based on sqrt
FS.nearOpt.fvprs.FRST

The near-optimal reduction algorithm based on fuzzy rough set theory
D.max.discernibility.matrix.RST

The maximal discernibility algorithm