A framework of combining Markov model with association rules for predicting web page accesses
Paper
Paper/Presentation Title | A framework of combining Markov model with association rules for predicting web page accesses |
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Presentation Type | Paper |
Authors | Khalil, Faten (Author), Li, Jiuyong (Author) and Wang, Hua (Author) |
Editors | Christen, Peter, Kennedy, Paul J., Li, Jiuyong, Simoff, Simeon J. and Williams, Graham J. |
Journal or Proceedings Title | Proceedings of the 5th Australasian Data Mining Conference (AusDM 2006): Data Mining and Analytics 2006 |
Number of Pages | 8 |
Year | 2006 |
Place of Publication | Canberra, Australia |
ISBN | 1920682422 |
Web Address (URL) of Paper | http://crpit.com/Vol61.html |
Conference/Event | 5th Australasian Conference on Data Mining and Analystics (AusDM 2006) |
Event Details | 5th Australasian Conference on Data Mining and Analystics (AusDM 2006) Parent Australasian Data Mining Conference (AusDM) Event Date 29 to end of 30 Nov 2006 Event Location Sydney, Australia |
Abstract | 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 |
Keywords | Association rules, Markov models, prediction |
ANZSRC Field of Research 2020 | 460806. Human-computer interaction |
490501. Applied statistics | |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9y0xz/a-framework-of-combining-markov-model-with-association-rules-for-predicting-web-page-accesses
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