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Integrating Markov Model with KNN Classification for Web Page Prediction

by J. S. Raikwal, Rahul Singhai, Kanak Saxena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 22
Year of Publication: 2013
Authors: J. S. Raikwal, Rahul Singhai, Kanak Saxena
10.5120/10226-3612

J. S. Raikwal, Rahul Singhai, Kanak Saxena . Integrating Markov Model with KNN Classification for Web Page Prediction. International Journal of Computer Applications. 61, 22 ( January 2013), 11-15. DOI=10.5120/10226-3612

@article{ 10.5120/10226-3612,
author = { J. S. Raikwal, Rahul Singhai, Kanak Saxena },
title = { Integrating Markov Model with KNN Classification for Web Page Prediction },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 22 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number22/10226-3612/ },
doi = { 10.5120/10226-3612 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:17.200591+05:30
%A J. S. Raikwal
%A Rahul Singhai
%A Kanak Saxena
%T Integrating Markov Model with KNN Classification for Web Page Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 22
%P 11-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

World Wide Web is growing rapidly in recent years. User’s experience on the internet can be improved by minimizing user’s web access latency. This can be done by predicting the next step taken by user towards the accessing of web page in advance, so that the predicted web page can be prefetched and cached. So to improve the quality of web services, it is required to analyze the user web navigation behavior. Analysis of user web navigation behavior is achieved through modeling web navigation history. Markov model is widely used to model the user web navigation sessions. Although traditional Markov models have helped predict user access behavior, they have serious limitations In this paper, we analyze and study Markov model and all-Kth Markov model in Web prediction. We propose new two-tier prediction frameworks that classify the user sessions, based on the KNN algorithm and then the Kth Markov Model is applied to predict the next web page. We show that such framework can improve the prediction time without compromising prediction accuracy and provides better performance over build time, search time, memory used and error rate.

References
  1. Deshpande, M. and Karypis, G. 2001. Selective Markov Models for Predicting Web-Page Accesses. First SIAM International Conference on Data Mining, 1-15.
  2. Dongshan, X. and Xi’an, S. J. 2002. A new markov model for web access prediction. Computing in science and engineering, vol 4, issue 6, 34-39.
  3. Sherchan, W. 2011. A Trust Prediction Model for Service Web, International Joint Conference of IEEE TrustCom-11, 258-265.
  4. Khanchana, R and Punithavalli, M. 2011. An Efficient Web Page Prediction Based on Access Time-Length and Frequency, 3rd International Conference on Electronics Computer Technology (ICECT), Kanyakumari.
  5. Nigam, B. and Jain, S. 2010. Analysis of markov model on different web prefetching and caching schemes. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 78-83, Coimbatore, India.
  6. Borges, J. and Levene, M. 2007. Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions. IEEE Transactions on knowledge and data engineering, vol. 19, no. 4 .
  7. Nigam, B. 2010. Generating a New Model for Predicting the Next Accessed Web Page in Web Usage Mining, Third International Conference on Emerging Trends in Engineering and Technology, 485-490.
  8. Popa, R. and Levendovszky, T. 2008. Marcov Models for Web Access Prediction. 8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, 539-550.
  9. Fan, W. I., Chiu, I. and Lin, J. 2005. Prediction of the Intention of Purchase of the User Surfing on the Web Using Hidden Markov Model. International Conference on Services Systems and Services Management, Vol. 1.
  10. Awad, M.A. and Khalil, I. 2012. Prediction of User’s Web-Browsing Behavior: Application of Markov Model. IEEE Transactions on systems, man, and cybernetics—part b: cybernetics, Vol 42, No. 4.
  11. Ahmed, N. S., Zafar, S. Asghar, S. 2010. Sequential Pattern Finding: A Survey. International Conference on Information and Emerging Technologies (ICIET), 244-249.
  12. Sarukkai, R. 2000. Link prediction and path analysis using markov chains. Ninth International World Wide Web Conference, Volume 33 Issue 1-6.
  13. Poonam Kaushal, Hybrid Markov model for better prediction of web page International Journal of Scientific and Research Publications , Volume 2, Issue 8, August 2012, ISSN 225-3153.
Index Terms

Computer Science
Information Sciences

Keywords

Marko model Kth Marko model Web page prediction User's browsing behavior Classification Algorithms Web mining

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