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

Chimphlee, Siriporn; Salim, Naomie; Ngadiman, Mohd. Salihin; Chimphlee, Witcha

doi:10.3923/itj.2010.774.781

Cited by 5 publications

(11 citation statements)

References 7 publications

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“…Third, many of the papers have addressed the integration Markov model, Association rules and clustering method. This model could provide better predictions than using each approach individually [5]. It can be inferred that most researchers use Markov models for this type of prediction and is thus, mostly preferred.…”

Section: Introductionmentioning

confidence: 99%

“…The most widely used approach is web usage mining that includes many models like the Markov models, association rules and clustering [5]. However, there are some challenges with the current state of the art solutions when it comes to accuracy, coverage and performance.…”

Section: Introductionmentioning

confidence: 99%

“…They have also proposed, on the other hand, solutions for prediction ambiguities. Chimphlee [5] has proposed a hybrid prediction model (HyMFM) that integrates Markov model, Association rules and Fuzzy Adaptive Resonance Theory (Fuzzy ART) clustering all together. These three approaches are integrated to maximize their strengths.…”

Section: Introductionmentioning

confidence: 99%

“…First, many of the papers proposed using association rules or Markov models for next page predictions [1,7,[9][10][11]. Second, many papers have addressed the uses of combining both methodologies [2][3][4][5]. Third, many of the papers have addressed the integration Markov model, Association rules and clustering method.…”

Section: Introductionmentioning

confidence: 99%

“…They have concluded that the latest substring representation, coupled with the pessimistic-selection method, gives the best prediction performance. 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. In Markov models, the transition probability matrix is built making and predictions for web sessions are straight forward.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improving Web Page Prediction Using Default Rule Selection

Pamutha¹,

Kimpan²,

Chimplee³

et al. 2012

IJACSA

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Web Page Prediction Using Default Rule Selection

Pamutha¹,

Kimpan²,

Chimplee³

et al. 2012

IJACSA

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Exploring the Academic Collaboration Chance in Open Research Community

Xu¹

2012

Information Technology J.

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An Efficient and Accurate Discovery of Frequent Patterns Using Improved WARM to Handle Large Web Log Data

Mary¹,

Malarvizhi²

2014

International Journal of Information Technology and Web Engineering

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In the booming era of Internet, web search is inevitable to everyone. In web search, mining frequent pattern is a challenging one, particularly when handling tera byte size databases. Finding solution for these issues have primarily started attracting the key researchers. Due to high the demand in finding the best search methods, it is very important and interesting to predict the user's next request. The number of frequent item sets and the database scanning time should be reduced for fast generating frequent pattern mining. It fulfills user's accurate need in a magic of time and offers a customized navigation. Association Rule mining plays key role in discovering associated web pages and many researchers are using Apriori algorithm with binary representation in this area. But it does not provide best solution for finding navigation order of web pages. To overcome this, weighted Apriori was introduced. But still, it is difficult to produce most favorable results especially in large databases. In the effort of finding best solution, the authors have proposed a novel approach which combines weighted Apriori and dynamic programming. The conducted experiments so far, shows' better tracking of maintaining navigation order and gives the confidence of making the best possible results. The proposed approach enriches the web site effectiveness, raises the knowledge in surfing, ensures prediction accuracies and achieves less complexity in computing with very large databases.

show abstract

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

Cited by 5 publications

References 7 publications

Improving Web Page Prediction Using Default Rule Selection

Improving Web Page Prediction Using Default Rule Selection

Exploring the Academic Collaboration Chance in Open Research Community

An Efficient and Accurate Discovery of Frequent Patterns Using Improved WARM to Handle Large Web Log Data

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