Prediction of Web User’s Browsing Behavior using All Kth Markov model and CSB-mine
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2017 by IJCTT Journal | ||
| Volume-43 Number-1 |
||
| Year of Publication : 2017 | ||
| Authors : Neha V. Patil, Dr. Hitendra D.Patil | ||
| DOI : 10.14445/22312803/IJCTT-V43P110 |
Neha V. Patil, Dr. Hitendra D.Patil "Prediction of Web User’s Browsing Behavior using All Kth Markov model and CSB-mine". International Journal of Computer Trends and Technology (IJCTT) V43(1):68-74, January 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Web Usage Mining (WUM) provides interesting rules and patterns of user web browsing behavior and prediction technique is very useful where the next set of web pages are predicted based on history of visited web pages. Nowadays use of the web and so the internet traffic has increased, so prediction of user web behavior plays important role in various applications like smart phones, recommendation systems and web personalization, etc. Various prediction models have been proposed based on Markov models, Association Rule Mining (ARM), etc. The Specific order of the Markov model cannot predict for a session that was not observed previously in the training set. ARM endures scalability issue, originates from generating item sets. Proposed system first preprocess raw web log, construct sessions for different users, then All Kth Markov model and using Conditional Sequence Base (CSB-mine), Sequential Access Patterns based model both are used individually to predict the next page that a user may visit.
References
[1] Mamoun A. Awad and Issa Khalil, "Prediction of User’s Web-Browsing Behavior: Application of Markov Model," IEEE Trans. on Systems, Man, and Cybernetics., vol. 42, no. 4, pp. 1131-1142, August 2012.
[2] Srivastava, T., PrasannaDesikan, and Vipin Kumar, "Web mining–concepts, applications and research directions," Foundations and Advances in Data Mining. Springer Berlin Heidelberg, pp. 275-307, 2005.
[3] RajniPamnani and PramilaChawan, "Web Usage Mining: A Research Area in Web Mining," in Proc. ISCET, Jun. 2013.
[4] T. Joachims, D. Freitag, and T. Mitchell, "WebWatcher: A tour guide for the World Wide Web," in Proc. I JCAI, pp. 770–777, 1997.
[5] Nizar R. Mabroukeh and C. I. Ezeife, " Semantic-rich Markov Models for Web Prefetching," in Proc. IEEE International Conf. on Data Mining Workshops, pp. 200-207, Jun. 2009.
[6] Deshpande Mukund and George Karypis, "Selective Markov models for predicting Web page accesses," ACM Transactions on Internet Technology (TOIT), pp. 163-184, 2004.
[7] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, "Effective personalization based on association rule discovery from Web usage data,"in Proc. ACM Workshop WIDM,Atlanta, GA, Nov. 2001.
[8] M. Awad and L. Khan, "Web navigation prediction using multipleevidence combination and domain knowledge,"IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 37, no. 6, pp. 1054–1062, Nov. 2007.
[9] F. Khalil, J. Li and H. Wang, "An Integrated Model for Next Page Access Prediction,"International Journal of Knowledge and Web Intelligence, vol. 1, no.1/ 2, pp. 48-80, August 2009.
[10] http://www.w3.org/Daemon/User/Config/Logging.html.
[11] R. Cooley, B. Mobasher, and J. Srivastava, "Data preparation for miningWorld Wide Web browsing patterns,"J. Knowl. Inf. Syst., vol. 1, no. 1,pp. 5–32, 1999.
[12] B. Y. Zhou, S. C. Hui and A. C. M. Fong, "Efficient Sequential Access Pattern Mining for Web Recommendations,"International Journal of Knowledge based and Intelligent Engineering Systems, ACM, vol. 10, no. 2, pp. 155–168, April 2006.
[13] Internet Traf?c Archive. [Online]. Available: http://ita.ee.lbl.gov/html/traces.html.
Keywords
WUM, Web session, Web browsing behavior, web prediction, All Kth Markov, CSB-mine