Man pages for RoughSets
Data Analysis Using Rough Set and Fuzzy Rough Set Theories
| 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 |
| BC.boundary.reg.RST | Computation of a boundary region |
| BC.discernibility.mat.FRST | The decision-relative discernibility matrix based on fuzzy... |
| BC.discernibility.mat.RST | Computation of a decision-relative discernibility matrix... |
| 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... |
| BC.LU.approximation.FRST | The fuzzy lower and upper approximations based on fuzzy rough... |
| BC.LU.approximation.RST | Computation of lower and upper approximations of decision... |
| 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 |
| B.Introduction-FuzzyRoughSets | Introduction to Fuzzy Rough Set Theory |
| 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... |
| 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... |
| D.local.discernibility.heuristic.RST | Supervised discretization based on the local discernibility... |
| FS.all.reducts.computation | A function for computing all decision reducts of a decision... |
| 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... |
| FS.greedy.heuristic.superreduct.RST | The greedy heuristic method for determining superreduct based... |
| FS.nearOpt.fvprs.FRST | The near-optimal reduction algorithm based on fuzzy rough set... |
| FS.one.reduct.computation | Computing one reduct from a discernibility matrix |
| FS.permutation.heuristic.reduct.RST | The permutation heuristic algorithm for computation of a... |
| 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 |
| 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... |
| MV.mostCommonValResConcept | The most common value or mean of an attribute restricted to a... |
| predict.RuleSetFRST | The predicting function for rule induction methods based on... |
| 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.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 |
| RoughSetData | Data set of the package |
| RoughSets-package | 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... |
| SF.read.DecisionTable | Reading tabular data from files. |
| sub-.RuleSetRST | The '[.' method for '"RuleSetRST"' objects |
| summary.IndiscernibilityRelation | The summary function for an indiscernibility relation |
| summary.LowerUpperApproximation | The summary function of lower and upper approximations based... |
| 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 |
