Title: An integrated model for next page access prediction

Authors: F. Khalil, J. Li, H. Wang

Addresses: Department of Mathematics and Computing, University of Southern Queensland, Toowoomba 4350, Australia. ' School of Computer and Information Science, University of South Australia, Mason Lakes 5095, Australia. ' Department of Mathematics and Computing, University of Southern Queensland, Toowoomba 4350, Australia

Abstract: Accurate next web page prediction benefits many applications, e-business in particular. The most widely used techniques for this purpose are Markov Model, association rules and clustering. However, each of these techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper presents an improved prediction accuracy and state space complexity by using novel approaches that combine clustering, association rules and Markov Models. The three techniques are integrated together to maximise their strengths. The integration model has been shown to achieve better prediction accuracy than individual and other integrated models.

Keywords: next web page prediction; Markov models; association rules; clustering; e-business; electronic business; prediction accuracy; state space complexity; web pages; internet.

DOI: 10.1504/IJKWI.2009.027925

International Journal of Knowledge and Web Intelligence, 2009 Vol.1 No.1/2, pp.48 - 80

Published online: 19 Aug 2009 *

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