Hybrid web page prediction model for predicting a user's next access

dc.contributor.authorS. Chimphlee
dc.contributor.authorN. Salim
dc.contributor.authorM.S.B. Ngadiman
dc.contributor.authorW. Chimphlee
dc.date.accessioned2025-03-10T07:37:41Z
dc.date.available2025-03-10T07:37:41Z
dc.date.issued2010
dc.description.abstractThe web user sessions are clustered with incorporating the sequence of web page visits. A sequence-based clustering is developed by proposing new sequence representations and new similarity measures. The resulting sequence representation allows for calculation of similarity between web user sessions and then, can be used as input of clustering algorithms. This study proposed a hybrid prediction model (HyMFM) that integrates Markov model, Association rules and Fuzzy Adaptive Resonance Theory (Fuzzy ART) clustering together. The three approaches are integrated to maximize their strengths. A series of experiments was conducted to investigate whether, clustering performance is affected by different sequence representations and different similarity measures. This model could provide better prediction than using each approach individually. © 2010 Asian Network for Scientific Information.
dc.identifier.citationInformation Technology Journal
dc.identifier.doi10.3923/itj.2010.774.781
dc.identifier.issn18125638
dc.identifier.scopus2-s2.0-77955735537
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5044
dc.languageEnglish
dc.publisherAsian Network for Scientific Information
dc.rights.holderScopus
dc.subjectAssociation rules
dc.subjectFuzzy adaptive resonance theory
dc.subjectMarkov model
dc.subjectWeb page prediction
dc.subjectWeb usage mining
dc.titleHybrid web page prediction model for predicting a user's next access
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77955735537&doi=10.3923%2fitj.2010.774.781&partnerID=40&md5=1937e5b302dd916057dba76e23cec330
oaire.citation.endPage781
oaire.citation.issue4
oaire.citation.startPage774
oaire.citation.volume9
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