Using association rules and Markov model for predict next access on Web usage mining

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dc.contributor.authorSiriporn Chimphlee
dc.contributor.authorNaomie Salim
dc.contributor.authorMohd Salihin Bin Ngadiman
dc.contributor.authorWitcha Chimphlee
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2006
dc.description.abstractPredicting 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 prediction based on past visitor behavior and compare it association rules technique. In these approaches, sequences of user requests are collected by the session identification technique, 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. © 2006 Springer.
dc.identifier.citationAdvances in Systems, Computing Sciences and Software Engineering - Proceedings of SCSS 2005
dc.identifier.doi10.1007/1-4020-5263-4-58
dc.identifier.isbn1402052626; 978-140205262-0
dc.identifier.scopus2-s2.0-84862209764
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5075
dc.languageEnglish
dc.rights.holderScopus
dc.subjectAssociation rules
dc.subjectMarkov model
dc.subjectPrediction
dc.titleUsing association rules and Markov model for predict next access on Web usage mining
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84862209764&doi=10.1007%2f1-4020-5263-4-58&partnerID=40&md5=c78313a142a67a84e5a799fc6874ef9b
oaire.citation.endPage376
oaire.citation.startPage371
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