DiscernibilityMatrix-class: Class '"DiscernibilityMatrix"'

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

Description

A discernibility matrix identifies the differences in condition values for each pair of rules in a decision table. Its main function is to help in the calculation of the core of the decision table rules. Objects of this class are not meant to be built by users, instead they should be created using a Decision Table object and the method computeDiscernibilityMatrix.

Objects from the Class

Objects can be created by calls of the form new("DiscernibilityMatrix", discernibilityMatrix). Objects of this class are not meant to be built by users, instead they should be created using a Decision Table object and the method computeDiscernibilityMatrix.

Slots

discernibilityMatrix:

Object of class "array"

Methods

computeCore

signature(object = "DiscernibilityMatrix"): ...

getDiscernibilityMatrix

signature(object = "DiscernibilityMatrix"): ...

initialize

signature(.Object = "DiscernibilityMatrix"): ...

print

signature(x = "DiscernibilityMatrix"): ...

show

signature(object = "DiscernibilityMatrix"): ...

Note

This is not a complete implementation of Rough Set theory; instead it is just the application of the theory to decision table simplification also known as knowledge reduction.

Author(s)

Alber Sanchez

References

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

See Also

DecisionTable ConditionReduct ValueReduct

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)
dm <- computeDiscernibilityMatrix(dt)

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