Predicting next page access by Markov models and association rules on web log data
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 we...
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| Format: | Article |
| Language: | en |
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Walden University
2006
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| Online Access: | http://eprints.utm.my/8408/1/NaomieSalim2006_PredictingNextPagebyMarkovModels.pdf http://eprints.utm.my/8408/ http://www.ijamt.org/ijamt/ |
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| _version_ | 1845472000633470976 |
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| author | Chimphlee, S. Salim, N. B. Ngadiman, M. S. Chimphlee, W. Srinoy, S. |
| author_facet | Chimphlee, S. Salim, N. B. Ngadiman, M. S. Chimphlee, W. Srinoy, S. |
| author_sort | Chimphlee, S. |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utm.eprints-8408 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2006 |
| publisher | Walden University |
| record_format | eprints |
| spelling | my.utm.eprints-84082017-10-24T04:36:25Z http://eprints.utm.my/8408/ Predicting next page access by Markov models and association rules on web log data Chimphlee, S. Salim, N. B. Ngadiman, M. S. Chimphlee, W. Srinoy, S. QA75 Electronic computers. Computer science 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. Walden University 2006 Article PeerReviewed application/pdf en http://eprints.utm.my/8408/1/NaomieSalim2006_PredictingNextPagebyMarkovModels.pdf 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 http://www.ijamt.org/ijamt/ |
| spellingShingle | QA75 Electronic computers. Computer science Chimphlee, S. Salim, N. B. Ngadiman, M. S. Chimphlee, W. Srinoy, S. Predicting next page access by Markov models and association rules on web log data |
| title | Predicting next page access by Markov models and association rules on web log data |
| title_full | Predicting next page access by Markov models and association rules on web log data |
| title_fullStr | Predicting next page access by Markov models and association rules on web log data |
| title_full_unstemmed | Predicting next page access by Markov models and association rules on web log data |
| title_short | Predicting next page access by Markov models and association rules on web log data |
| title_sort | predicting next page access by markov models and association rules on web log data |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/8408/1/NaomieSalim2006_PredictingNextPagebyMarkovModels.pdf http://eprints.utm.my/8408/ http://www.ijamt.org/ijamt/ |
| url_provider | http://eprints.utm.my/ |