A Classifier-CMAC Neural Network Model for Web Mining

The rapid growth of Web has made it a huge source of information which will make the availability of data easier and more efficient if its content is well organized. Automatic classification of Web pages is one of the major methods in the Web content mining (WCM) which can be of great value in the development and maintenance of Web directories. Based on the analysis done, CMAC neural network showed faster learning in high dimensional problems, but considering the heavy data on the Web, the main challenge in Web page classification is how to deal with high dimensional feature space which increase the memory required by CMAC neural network. In the present paper a classifier-CMAC neural network model is proposed for use in content based Web page classification which requires less memory. The results reveal that the proposed model is more useful than any other algorithms.