Combining web data mining techniques for web page access prediction
PhD Thesis
Title | Combining web data mining techniques for web page access prediction |
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Type | PhD Thesis |
Authors | |
Author | Khalil, Faten |
Supervisor | Wang, Hua |
Institution of Origin | University of Southern Queensland |
Qualification Name | Doctor of Philosophy |
Number of Pages | 197 |
Year | 2008 |
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 |
Keywords | web page access prediction; web usage mining; Markov model |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460508. Information retrieval and web search | |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9yvzz/combining-web-data-mining-techniques-for-web-page-access-prediction
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