Khalil, Faten and Wang, Hua and Li, Jiuyong (2007) Integrating Markov Model with clustering for predicting web page accesses. In: 13th Australasian World Wide Web Conference (AusWeb07), 30 June - 4 July 2007, Coffs Harbour, Australia.
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[Abstract]: Predicting the next page to be accessed by Web users has attracted a large amount of research work lately due to the positive impact of such prediction on different areas of Web based applications. One major technique applied for this intention is Markov model. Low order Markov models are coupled with low accuracy, whereas high order Markov models are associated with high state space complexity. This paper involves incorporating clustering techniques by dividing pre-processed data into meaningful clusters then performing low order Markov models to each cluster instead of the whole data sets. Different distance measures of k-means clustering algorithm are examined in order to find an optimal one. Experiments reveal that clustering of Web documents according to Web services improves the low order Markov models accuracy.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||Deposited in accordance with the copyright policy of the publisher.|
|Uncontrolled Keywords:||web page prediction; accuracy|
|Fields of Research (FOR2008):||08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining|
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
|Subjects:||280000 Information, Computing and Communication Sciences|
|Socio-Economic Objective (SEO2008):||UNSPECIFIED|
|Deposited On:||31 Mar 2008 14:13|
|Last Modified:||04 Apr 2012 12:03|
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- Integrating recommendation models for improved web page prediction accuracy. (deposited 19 Feb 2008 12:58)
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