# RoughSets v1.3-7

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## 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 Zdzis<c5><82>aw
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.

## Functions in RoughSets

Name | Description | |

BC.discernibility.mat.RST | Computation of a decision-relative discernibility matrix based on the rough set theory | |

A.Introduction-RoughSets | Introduction to Rough Set Theory | |

BC.discernibility.mat.FRST | The decision-relative discernibility matrix based on fuzzy rough set theory | |

B.Introduction-FuzzyRoughSets | Introduction to Fuzzy Rough Set Theory | |

BC.IND.relation.RST | Computation of indiscernibility classes based on the rough set theory | |

BC.IND.relation.FRST | The indiscernibility relation based on fuzzy rough set theory | |

BC.boundary.reg.RST | Computation of a boundary region | |

BC.negative.reg.RST | Computation of a negative region | |

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 | |

D.discretize.equal.intervals.RST | Unsupervised discretization into intervals of equal length. | |

D.global.discernibility.heuristic.RST | Supervised discretization based on the maximum discernibility heuristic | |

BC.positive.reg.RST | Computation of a positive region | |

BC.positive.reg.FRST | Positive region based on fuzzy rough set | |

C.POSNN.FRST | The positive region based fuzzy-rough nearest neighbor algorithm | |

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

D.discretization.RST | The wrapper function for discretization methods | |

FS.nearOpt.fvprs.FRST | The near-optimal reduction algorithm based on fuzzy rough set theory | |

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 | |

MV.mostCommonValResConcept | The most common value or mean of an attribute restricted to a concept | |

RI.AQRules.RST | Rule induction using the AQ algorithm | |

D.discretize.quantiles.RST | The quantile-based discretization | |

C.FRNN.FRST | The fuzzy-rough nearest neighbor algorithm | |

MV.missingValueCompletion | Wrapper function of missing value completion | |

D.local.discernibility.heuristic.RST | Supervised discretization based on the local discernibility heuristic | |

MV.conceptClosestFit | Concept Closest Fit | |

IS.FRPS.FRST | The fuzzy rough prototype selection method | |

FS.quickreduct.FRST | The fuzzy QuickReduct algorithm based on FRST | |

RI.CN2Rules.RST | Rule induction using a version of CN2 algorithm | |

FS.quickreduct.RST | QuickReduct algorithm based on RST | |

FS.permutation.heuristic.reduct.RST | The permutation heuristic algorithm for computation of a decision reduct | |

C.FRNN.O.FRST | The fuzzy-rough ownership nearest neighbor algorithm | |

FS.one.reduct.computation | Computing one reduct from a discernibility matrix | |

FS.DAAR.heuristic.RST | The DAAR heuristic for computation of decision reducts | |

FS.reduct.computation | The reduct computation methods based on RST and FRST | |

RI.GFRS.FRST | Generalized fuzzy rough set rule induction based on FRST | |

X.nOfConflicts | The discernibility measure | |

SF.asDecisionTable | Converting a data.frame into a DecisionTable object | |

X.laplace | Rule voting by the Laplace estimate | |

SF.applyDecTable | Apply for obtaining a new decision table | |

FS.all.reducts.computation | A function for computing all decision reducts of a decision system | |

IS.FRIS.FRST | The fuzzy rough instance selection algorithm | |

RoughSets-package | Getting started with the RoughSets package | |

RoughSetData | Data set of the package | |

X.entropy | The entropy measure | |

X.gini | The gini-index measure | |

predict.RuleSetFRST | The predicting function for rule induction methods based on FRST | |

MV.deletionCases | Missing value completion by deleting instances | |

SF.asFeatureSubset | Converting custom attribute name sets into a FeatureSubset object | |

SF.read.DecisionTable | Reading tabular data from files. | |

MV.mostCommonVal | Replacing missing attribute values by the attribute mean or common values | |

print.RuleSetRST | The print function for RST rule sets | |

RI.laplace | Quality indicators of RST decision rules | |

X.rulesCounting | Rule voting by counting matching rules | |

RI.indiscernibilityBasedRules.RST | Rule induction from indiscernibility classes. | |

print.FeatureSubset | The print method of FeatureSubset objects | |

X.ruleStrength | Rule voting by strength of the rule | |

summary.LowerUpperApproximation | The summary function of lower and upper approximations based on RST and FRST | |

predict.RuleSetRST | Prediction of decision classes using rule-based classifiers. | |

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

MV.globalClosestFit | Global Closest Fit | |

RI.LEM2Rules.RST | Rule induction using the LEM2 algorithm | |

summary.RuleSetFRST | The summary function of rules based on FRST | |

summary.RuleSetRST | The summary function of rules based on RST | |

RI.hybridFS.FRST | Hybrid fuzzy-rough rule and induction and feature selection | |

as.character.RuleSetRST | The as.character method for RST rule sets | |

as.list.RuleSetRST | The as.list method for RST rule sets | |

summary.IndiscernibilityRelation | The summary function for an indiscernibility relation | |

[.RuleSetRST | The [. method for "RuleSetRST" objects | |

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## Details

License | GPL (>= 2) |

Encoding | UTF-8 |

URL | https://github.com/janusza/RoughSets |

Date | 2019-12-15 |

LinkingTo | Rcpp |

RoxygenNote | 6.1.0 |

NeedsCompilation | yes |

Packaged | 2019-12-14 23:22:40 UTC; bergmeir |

Repository | CRAN |

Date/Publication | 2019-12-15 06:30:19 UTC |

suggests | class |

depends | Rcpp |

Contributors | Chris Cornelis, Francisco Herrera, Lala Septem Riza, Andrzej Janusz, Jose Manuel Benitez, Sebastian Stawicki, Dominik <c5><9a>l<c4><99>zak |

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