computeSupportConsistency: COMPUTE SUPPORT CONSISTENCY

In albhasan/RoughSetKnowledgeReduction: Simplification of Decision Tables using Rough Sets

Description

It computes the support and consistency of the rules in the Value Reduct object. For each rule in the Value Reduct object, support is the number of decision table rules to which the value reduct rule conditions apply divided by the number of rules in the decision table object. For each rule in the Value Reduct object, consistency is the number of rules to which the value reduct condition and decision applies divided by the number of rules of the Decision Table object to which the value reduct rule conditions apply.

Usage

computeSupportConsistency(object, decisionTable)

Arguments

object	A Value Reduct object
decisionTable	A Decision Table object

Value

It returns a numeric matrix which contains the Value Reduct object representation and the support and consistency values of each rule.

Author(s)

Alber Sanchez [e-mail]

References

Pawlak, Zdzislaw 1991 Rough Sets: Theoretical Aspects of Reasoning About Data Dordrecht: Kluwer Academic Publishing.

See Also

ValueReduct-class,classifyDecisionTable

Examples

exampleMatrix2 <- matrix(c(1,1,0,1,1,2,2,0,0,0,1,1,1,2,0,
0,0,0,0,0,2,1,0,0,1,2,2,2,1,1,0,0,2,2,2),ncol = 5)
dt <- new(Class="DecisionTable",decisionTable = exampleMatrix2)
dtUnique <- removeDuplicatedRulesDT(dt)
cr <- new(Class="ConditionReduct",decisionTable = dtUnique,columnIds=c(1,2,4,5))
cr <- removeDuplicatedRulesCR(cr)
vr <- computeValueReduct(cr)
vr <- removeDuplicatedRulesVR(vr)
mat <- computeSupportConsistency(vr,dt)
print(mat)

albhasan/RoughSetKnowledgeReduction documentation built on May 11, 2019, 10:29 p.m.