Combining web data mining techniques for web page access prediction

PhD Thesis


Khalil, Faten. 2008. Combining web data mining techniques for web page access prediction. PhD Thesis Doctor of Philosophy. University of Southern Queensland.
Title

Combining web data mining techniques for web page access prediction

TypePhD Thesis
Authors
AuthorKhalil, Faten
SupervisorWang, Hua
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages197
Year2008
Abstract

[Abstract]: Web page access prediction gained its importance from the ever increasing number of e-commerce Web information systems and e-businesses. Web page prediction, that involves personalising the Web users’ browsing experiences, assists Web masters in the improvement of the Web site structure and helps Web users in navigating the site and accessing the information they need. The most widely used approach for this purpose is the pattern discovery process of Web usage mining that entails many techniques like Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page to
be accessed by theWeb user based on the user’s previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especially
when it comes to accuracy and space complexity. This dissertation achieves better accuracy as well as less state space complexity and rules generated by performing
the following combinations. First, we combine low-order Markov model and association rules. Markov model analysis are performed on the data sets. If the Markov model prediction results in a tie or no state, association rules are used for prediction. The outcome of this integration is better accuracy, less Markov model state space complexity and less number of generated rules than using each of the methods individually. Second, we integrate low-order Markov model and clustering. The data sets are clustered and Markov model analysis are performed on
each cluster instead of the whole data sets. The outcome of the integration is better accuracy than the first combination with less state space complexity than higher
order Markov model. The last integration model involves combining all three techniques together: clustering, association rules and low-order Markov model. The data sets are clustered and Markov model analysis are performed on each cluster. If the Markov model prediction results in close accuracies for the same item, association rules are used for prediction. This integration model achieves
better Web page access prediction accuracy, less Markov model state space complexity and less number of rules generated than the previous two models.

Keywordsweb page access prediction; web usage mining; Markov model
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
460508. Information retrieval and web search
Byline AffiliationsDepartment of Mathematics and Computing
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Related outputs

An integrated model for next page access prediction
Khalil, Faten, Li, Jiuyong and Wang, Hua. 2009. "An integrated model for next page access prediction." International Journal of Knowledge and Web Intelligence. 1 (1/2), pp. 48-80. https://doi.org/10.1504/IJKWI.2009.027925
Integrating recommendation models for improved web page prediction accuracy
Khalil, Faten, Li, Jiuyong and Wang, Hua. 2008. "Integrating recommendation models for improved web page prediction accuracy." Dobbie, Gillian and Mans, Bernard (ed.) ACSC 2008: 31st Australasian Computer Science Conference. Wollongong, Australia 22 - 25 Jan 2008 Sydney, Australia.
Integrating Markov Model with clustering for predicting web page accesses
Khalil, Faten, Wang, Hua and Li, Jiuyong. 2007. "Integrating Markov Model with clustering for predicting web page accesses." 13th Australasian World Wide Web Conference (AusWeb 2007). Coffs Harbour, Australia 30 Jun - 04 Jul 2007 Australia.
Integrating recommendation models for improved web page prediction accuracy
Khalil, Faten, Wang, Hua and Li, Jiuyong. 2007. "Integrating recommendation models for improved web page prediction accuracy." 13th Australasian World Wide Web Conference (AusWeb 2007). Coffs Harbour, Australia 30 Jun - 04 Jul 2007 Australia.
A framework of combining Markov model with association rules for predicting web page accesses
Khalil, Faten, Li, Jiuyong and Wang, Hua. 2006. "A framework of combining Markov model with association rules for predicting web page accesses." Christen, Peter, Kennedy, Paul J., Li, Jiuyong, Simoff, Simeon J. and Williams, Graham J. (ed.) 5th Australasian Conference on Data Mining and Analystics (AusDM 2006). Sydney, Australia 29 - 30 Nov 2006 Canberra, Australia.