Abstract
Mining user patterns of web log files can provide significant and useful informative knowledge. A large amount of research has been done in trying to predict correctly the pages a user will most likely request next. Markov models are the most commonly used approaches for this type of web access prediction. Web page prediction requires the development of models that can predict a user’s next access to a web server. Many researchers have proposed a novel approach that integrates Markov models, association rules and clustering in web site access predictability. The low order Markov models provide higher coverage, but these are couched in ambiguous rules. In this paper, we introduce the use of default rule in resolving web access ambiguous predictions. This method could provide better prediction than using the individual traditional models. The results have shown that the default rule increases the accuracy and model-accuracy of web page access predictions. It also applies to association rules and the other combined models.
Highlights
The rapid expansion of the World Wide Web has created an unprecedented opportunity to disseminate and gather information online
Markov models are used in identifying the page to be accessed by the Web site user based on the sequence of their previously accessed pages
We experimentally evaluated the performance of the proposed approach firstorder Markov model and construct the predictive model
Summary
The rapid expansion of the World Wide Web has created an unprecedented opportunity to disseminate and gather information online. Li and Wang [1] have studied different associationrule based methods for web request predictions In this work, they have examined two important dimensions in building prediction models, namely, the type of antecedents of rules and the criteria for selecting prediction rules. The authors [2,3,4,5] have applied the latest substring representation using the most-confident selection method to building association-rule based prediction models from web-log data using association rules. Researchers have proposed different methods in building association-rule based prediction models using web logs, but none had yielded significant results. We propose the default rule in resolving ambiguous predictions This method could provide better prediction than using the traditional models individually. The rest of this paper is organized as follows: Sec. Related Works Sec. Markov Models Sec. Ambiguous rules Sec. Experimental Setup and Result Sec. 6 Conclusion www.ijacsa.thesai.org
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