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**RoughSets**: Data Analysis Using Rough Set and Fuzzy Rough Set Theories**X.nOfConflicts**: The discernibility measure

# The discernibility measure

### Description

It is an auxiliary function for the `qualityF`

parameter in the `FS.greedy.heuristic.reduct.RST`

and `FS.greedy.heuristic.superreduct.RST`

functions.

### Usage

1 | ```
X.nOfConflicts(decisionDistrib)
``` |

### Arguments

`decisionDistrib` |
an integer vector corresponding to a distribution of decision attribute values |

### Value

a numeric value indicating a number of conflicts in a decision attribute

### Author(s)

Andrzej Janusz

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- 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.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.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