Application of Rough Set Theory in Data Mining for Decision Support Systems (DSSs)

Decision support systems (DSSs) are prevalent information systems for decision making in many competitive business environments. In a DSS, decision making process is intimately related to some factors which 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 are 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. Keyword: Data Mining, Knowledge Discovery, Rough Set Theory.

[1]  Ian Witten,et al.  Data Mining , 2000 .

[2]  Andrew Kusiak,et al.  Rough set theory: a data mining tool for semiconductor manufacturing , 2001 .

[3]  Zdzisław Pawlak,et al.  ROUGH CONTROL APPLICATION OF ROUGH SET THEORY TO CONTROL , 1996 .

[4]  Johannes Gehrke,et al.  Mining Very Large Databases , 1999, Computer.

[5]  Janusz A. Starzyk,et al.  High range resolution radar target classification: a rough set approach , 2001 .

[6]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[7]  Zdzislaw Pawlak,et al.  AI AND INTELLIGENT INDUSTRIAL APPLICATIONS: THE ROUGH SET PERSPECTIVE , 2000, Cybern. Syst..

[8]  Bhavani Thuraisingham,et al.  Data Mining: Technologies, Techniques, Tools, and Trends , 1998 .

[9]  Peter J. Haas,et al.  Interactive data Analysis: The Control Project , 1999, Computer.

[10]  Andrew Kusiak,et al.  Data mining of printed-circuit board defects , 2001, IEEE Trans. Robotics Autom..

[11]  C. McGreavy,et al.  Data Mining and Knowledge Discovery for Process Monitoring and Control , 1999 .

[12]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[13]  Laks V. S. Lakshmanan,et al.  Constraint-Based Multidimensional Data Mining , 1999, Computer.

[14]  P R Limb,et al.  Data Mining — Tools and Techniques , 1996 .

[15]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[16]  Andrew Kusiak,et al.  Data mining in engineering design: a case study , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[17]  Li Pheng Khoo,et al.  Feature extraction using rough set theory and genetic algorithms--an application for the simplification of product quality evaluation , 2002 .