Universiti Teknologi Malaysia Institutional Repository

Predicting next page access by Markov models and association rules on web log data

Chimphlee, S. and Salim, N. and B. Ngadiman, M. S. and Chimphlee, W. and Srinoy, S. (2006) Predicting next page access by Markov models and association rules on web log data. The International Journal of Applied Management and Technology, 4 (1). pp. 139-154. ISSN 1544-4740


Official URL: http://www.ijamt.org/ijamt/


Mining user patterns of log file can provide significant and useful informative knowledge. A large amount of the research has been concentrated on trying to correctly predict the pages a user will request. This task requires the development of models that can predict a user’s next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site based on past visitor behavior compare with association rules technique. This algorithm has been used to cluster Web site with similar transition behaviors and compares the transition matrix to an optimal size for efficient used to further improve the efficiency of prediction. From this comparison we propose a best overall method and empirically test the proposed model on real web logs.

Item Type:Article
Uncontrolled Keywords:Web mining, Markov model, association rule, prediction
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:8408
Deposited On:04 May 2009 07:49
Last Modified:24 Oct 2017 04:36

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