An efficient approach to Web page classification using non-linear cellular automata

In this paper, we propose a Cellular Automata (CA) based implementation in classification for handling huge amount of data on the Web in an efficient way. We concentrate on Multiple Attractor Cellular Automata (MACA) as well as Single Cycle Multiple Attractor Cellular Automata (SMACA), since these are responsible for classifying various types of patterns. CA based implementation results in minimization of storage space needed for storing the downloaded Web pages within a Search Engine. In this paper we are going to use the 3-Neighborhood concept of CA for classification purpose. Efficiency of our approach lies within the usage of CA as a classifier in different forms. Experimental results demonstrate our approach with a higher efficiency level.