Using Modularity with Rough Decision Models

Many real world applications need to deal with imprecise data. Therefore, there is a need for new techniques which can manage such imprecision. Computational Intelligence (CI) techniques are the most appropriate for dealing with imprecise data to help decision makers. It is well known that soft computing techniques like genetic algorithms, neural networks, and fuzzy logic are effective in dealing with problems without explicit model and characterized by uncertainties Using fuzzy set theory considered as major techniques, which allows decision makers to take a good decision using imprecise inexact data and knowledge. Now using rough set is getting quite necessary to be used for its ability to mining such type of data. In this research, we are looking forward to propose a novel technique, which depends on the integration between fuzzy set concepts and rough set theory in mining relational databases. The proposed model allows introducing modularity mechanism, by building a virtual modular decision tables according to variety of decision makers points of view. And introduce decision grouping mechanism for getting the optimizing decision. This approach provides flexibility in decision making verifies all decision standards and determines decision requirements, through modularizing rough decision table, extraction of rough association rules and developing mechanisms for decision grouping.