Title: Clustering-based web page prediction

Authors: Ruma Dutta; Anirban Kundu; Debajyoti Mukhopadhyay

Addresses: Netaji Subhash Engineering College, West Bengal University of Technology, Garia, Kolkata, 700152, West Benagal, India. ' Kuang-Chi Institute of Advanced Technology, Software Building, No. 9 Gaoxin Zhong, 1st Road, High-Tech Industrial Estate, Nanshan District, Shenzhen, Guangdong, 518057, China. ' Maharashtra Institute of Technology, S. No. 124, Paud Road, Kothrud, Pune 411038, Maharashtra, India; Web Intelligence and Distributed Computing Research Lab (WIDiCoReL), GreenTower, C-9/1, Golf Green, Kolkata, 700095, India

Abstract: Web page prediction plays an important role by predicting and fetching probable web page of next request in advance, resulting in reducing the user latency. The users surf the internet either by entering URL or search for some topic or through link of same topic. For searching and for link prediction, clustering plays an important role. Besides the topic, navigational behaviour is not ignored. This paper proposes a web page prediction model giving significant importance to the user's interest using the clustering technique and the navigational behaviour of the user through Markov model. The clustering technique is used for the accumulation of the similar web pages. Similar web pages of same type reside in the same cluster, the cluster containing web pages have the similarity with respect to topic of the session. The clustering algorithms considered are K-means and K-mediods, where K is determined by HITS algorithm. Finally, the predicted web pages are stored in form of cellular automata to make the system more memory efficient.

Keywords: web page prediction; HITS algorithm; clustering; cellular automata; user latency; similar web pages; web page similarity; web search.

DOI: 10.1504/IJKWI.2011.045163

International Journal of Knowledge and Web Intelligence, 2011 Vol.2 No.4, pp.257 - 271

Published online: 07 Mar 2015 *

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