JISE


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


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

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