Mohammad Hossein Fazel Zarandi - Department of Industrial Engineering, Amir kabir University of Technology, Tehran, IRAN
Ismail Burhan Turksen - Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ont., Canada
Abolfazl Kazemi - Department of Industrial Engineering, Amir kabir University of Technology, Tehran, IRAN
Ali Babapour Atashgah - Department of Industrial Engineering, Amir kabir University of Technology, Tehran, IRAN

Decision support systems (DSSs) are prevalent information system tools for decision making in very competitive business environment. In a DSS, decision making process is intimately related to some factors that determine the quality of information systems and their related products. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers need some DSS tools for rapid decision making. In traditional approaches to decision making, usually scientific expertise together with statistical techniques have been needed to support the managers. However, these approaches are not able to handle the huge amount of real data, and the processes are usually very slow. Recently, several innovative facilities have been presented for decision-making process in enterprises. Presenting new techniques for development of huge databases, together with some heuristic models have enhanced the capabilities of DSSs to support managers in all levels of organizations. Today, data mining and knowledge discovery is considered as the main module of development of advanced DSSs. In this research, we use rough set theory for data mining for decision-making process in a DSS. The proposed approach concentrates on individual objects rather than population of the objects. Finally, a rule extracted from a data set and the corresponding features (attributes) is considered in modeling data mining.

Data Mining, Knowledge Discovery, Rough Set Theory, Decision Support Systems, Decision Making