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USING MODULARITY WITH ROUGH DECISION MODELS

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Author(s): Ahmed T. Shawky | Hesham A. Hefny | Ashraf H. Abd-Elwahab

Journal: International Journal of Artificial Intelligence & Applications
ISSN 0976-2191

Volume: 3;
Issue: 1;
Start page: 15;
Date: 2012;
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Keywords: Rough sets | Fuzzy sets | modularity | Data mining

ABSTRACT
Many real world applications need to deal with imprecise data. Therefore, there is a need for newtechniques which can manage such imprecision. Computational Intelligence (CI) techniques are the mostappropriate for dealing with imprecise data to help decision makers. It is well known that soft computingtechniques like genetic algorithms, neural networks, and fuzzy logic are effective in dealing with problemswithout explicit model and characterized by uncertainties Using fuzzy set theory considered as majortechniques, which allows decision makers to take a good decision using imprecise inexact data andknowledge. Now using rough set is getting quite necessary to be used for its ability to mining such type ofdata. In this research, we are looking forward to propose a novel technique, which depends on theintegration between fuzzy set concepts and rough set theory in mining relational databases. The proposedmodel allows introducing modularity mechanism, by building a virtual modular decision tables accordingto variety of decision makers points of view. And introduce decision grouping mechanism for getting theoptimizing decision. This approach provides flexibility in decision making verifies all decision standardsand determines decision requirements, through modularizing rough decision table, extraction of roughassociation rules and developing mechanisms for decision grouping.
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