Combining web data mining techniques for web page access prediction

Khalil, Faten (2008) Combining web data mining techniques for web page access prediction. [Thesis (PhD/Research)] (Unpublished)

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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.


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Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Doctor of Philosophy (PhD) thesis.
Depositing User: epEditor USQ
Faculty / Department / School: Historic - Faculty of Sciences - No Department
Date Deposited: 31 Aug 2008 23:46
Last Modified: 13 Jul 2014 05:30
Uncontrolled Keywords: web page access prediction; web usage mining; Markov model
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
URI: http://eprints.usq.edu.au/id/eprint/4341

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