Integrating Markov Model with clustering for predicting web page accesses

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

[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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher.
Depositing User: Dr Hua Wang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 31 Mar 2008 04:13
Last Modified: 02 Jul 2013 23:00
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
URI: http://eprints.usq.edu.au/id/eprint/4026

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