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Published in:
2006 | OriginalPaper | Chapter
Using Association Rules and Markov Model for Predit Next Access on Web Usage Mining
Authors : Siriporn Chimphlee, Naomie Salim, Mohd Salihin Bin Ngadiman, Witcha Chimphlee
Published in: Advances in Systems, Computing Sciences and Software Engineering
Publisher: Springer Netherlands
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Predicting the next request of a user as visits Web pages has gained importance as Web-based activity increases. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts 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 access prediciton based on past visitor behavior and compare it association rules technique. In these qpproaches, sequences of user requests based on past visitor behavior and compare it association rules technique. In these approaches, sequences of user requests are collected by the session identificaiton techinique, which distinguishes the requests for the same web page in different browses. We report experimental studies using real server log for comparison between methods and show that degree of precision.