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Hybrid Web Page Prediction Model for Predicting a User ’s Next Access


Article Information

Title: Hybrid Web Page Prediction Model for Predicting a User ’s Next Access

Authors: S. Chimphlee, N. Salim, M.S.B. Ngadiman, W. Chimphlee

Journal: Information Technology Journal

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Publisher: Asian Network for Scientific Information (ANSInet)

Country: Pakistan

Year: 2010

Volume: 9

Issue: 4

Language: English

DOI: 10.3923/itj.2010.774.781

Keywords: Web Usage MiningAssociation RulesMarkov modelfuzzy adaptive resonance theoryWeb page prediction

Categories

Abstract

The 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.


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