Determining the Similarity of Web Pages based on Learning Automata and Probabilistic Grammar

Zohreh Anari, Babak Anari


As the number of web pages increases, search for useful information by users on web sites will become more significant. By determining the similarity of web pages, search quality can be improved; hence, users can easily find their relevant information. In this paper, distributed learning automata and probabilistic grammar were used to propose a new hybrid algorithm in order to specify the similarity of web pages by means of web usage data. In the proposed algorithm, a Learning Automata (LA) for each web page is assigned which its function is to evaluate association rules extracted by hypertext system. This learning process continues until the similarity of web pages are determined. Experimental results demonstrate the efficiency of the proposed algorithm over other existing techniques.


Web Mining; Association Rules; Learning Automata; Distributed Learning Automata; Hypertext Probabilistic Grammar

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