computeDiscernibilityMatrix-methods

computeDiscernibilityMatrix,DecisionTable-method


 Methods for function <code>computeDiscernibilityMatrix</code>


methods

logic

rough

Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.

Alber Sanchez

RoughSetKnowledgeReduction

Simplification of Decision Tables using Rough Sets

computeDiscernibilityMatrix-methods function

<dl> <dt><code>signature(object = "DecisionTable")</code></dt><dd>
This method computes the discernibility matrix of a Decision Table object. A Discernibility Matrix object is a 3 dimension boolean array where each element represents if there is a difference in the same condition of each pair of rules in a Decision Table object. A discernibility matrix is useful for calculating the core of a decision table.
</dd> </dl>

computeDiscernibilityMatrix-methods: Methods for Function `computeDiscernibilityMatrix`

Description

Arguments

Methods