Previous 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

°@

Journal of Information Science and Engineering, Vol. 24 No. 4, pp. 1111-1126 (July 2008)

An Intelligent Data Mining Approach Using Neuro-Rough Hybridization to Discover Hidden Knowledge from Information Systems

Reza Sabzevari and Gh. A. Montazer*
Department of Mechatronics Engineering
Islamic Azad University of Qazvin
Qazvin, Iran
Student Member of Young Researchers°¶ Club (YRC)
*Department of Information Engineering
Tarbiat Modares University
P.O. Box: 14115-179, Tehran, Iran
E-mail: montazer@modares.ac.ir

In this paper we discuss on the necessity of applying data mining operators on information systems containing a set of variables which describe the characteristics and behaviors of a specific system and could be exploited in approximating system°¶s functionality. For the problem of function approximation, we developed a new approach combining two intelligent methods. At first we used an algorithm based on the notions of rough set theory as a preprocessor to our information system. Afterward an artificial neural network is employed as a function approximator to obtain values for decision attributes of information system while values of condition ones are passed to the network. This method has been applied to a real problem of approximating values for two hydraulic- geotechnical control variables of rubble mound breakwaters, and the results have been discussed.

Keywords: data mining, information systems, modeling, function approximation, rough sets theory, artificial neural networks

Full Text (•Ģ§Śņ…) Retrieve PDF document (200807_07.pdf)

Received August 25, 2006; revised January 15, 2007; accepted February 14, 2007.
Communicated by Suh-Yin Lee.
*Corresponding author: Gh. A. Montazer.