A framework of combining Markov model with association rules for predicting web page accesses

Khalil, Faten and Li, Jiuyong and Wang, Hua (2006) A framework of combining Markov model with association rules for predicting web page accesses. In: 5th Australasian Data Mining Conference (AusDM 2006), 29-30 Nov 2006, Sydney, Australia.


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The importance of predicting Web users' behaviour and their next movement has been recognised and discussed by many researchers lately. Association rules and Markov models are the most commonly used approaches for this type of prediction. Association rules tend to generate many rules, which result in contradictory predictions for a user session. Low order Markov models do not use enough user browsing history and therefore, lack accuracy, whereas, high or- der Markov models incur high state space complexity. This paper proposes a novel approach that integrates both association rules and low order Markov models in order to achieve higher accuracy with low state space complexity. A low order Markov model provides high coverage with low state space complexity, and association rules help achieve better accuracy

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher. Copyright 2006, Australian Computer Society, Inc. This pa- per appeared at Australasian Data Mining Conference (AusDM 2006), Sydney, December 2006. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 61. Peter Christen, Paul Kennedy, Jiuyong Li, Simeon Simoff and Graham Williams, ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:57
Last Modified: 02 Jul 2013 22:42
Uncontrolled Keywords: Association rules, Markov models, prediction
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080602 Computer-Human Interaction
01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
URI: http://eprints.usq.edu.au/id/eprint/2096

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