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An Improved CMAC Neural Network Model for Web Mining

In recent years, the application of Web technology is more and more mature, and all manner of Web sites and search engines are distributed on the Internet resulting in a huge source of information. Thus, if the Web information is not well organized, it is hard to find the information what a user actually need or interest in. Automatic classification of Web pages is an efficient method in Web Content Mining which can be of great value in the management of Web directories. Based on the analysis done, The Cerebellar Model Articulation Controller (CMAC) is an excellent classification technique, but when it is applied to deal with high-dimensional dataset such as the data on the Internet, the memory required increase intensively. This paper presents an improved CMAC model used in content based Web page classification which requires less memory. The experimental results show that the proposed model is highly effective in Web page classification.