RoughSetKnowledgeReduction: Simplification of Decision Tables using Rough Sets

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.

Version: 0.1
Depends: methods
Published: 2014-12-18
Author: Alber Sanchez
Maintainer: Alber Sanchez <a.sanchez at uni-muenster.de>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: RoughSetKnowledgeReduction results

Downloads:

Reference manual: RoughSetKnowledgeReduction.pdf
Vignettes: How to of the RoughSetKnowledgeReduction package
Package source: RoughSetKnowledgeReduction_0.1.tar.gz
Windows binaries: r-devel: RoughSetKnowledgeReduction_0.1.zip, r-release: RoughSetKnowledgeReduction_0.1.zip, r-oldrel: RoughSetKnowledgeReduction_0.1.zip
OS X El Capitan binaries: r-release: RoughSetKnowledgeReduction_0.1.tgz
OS X Mavericks binaries: r-oldrel: RoughSetKnowledgeReduction_0.1.tgz

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