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Reseach Article

User Navigation Pattern Prediction using Longest Common Subsequence

Published on May 2013 by Samir S. Shaikh, Pravin B. Landage, D. B. Kshirsagar
International Conference on Recent Trends in Engineering and Technology 2013
Foundation of Computer Science USA
ICRTET - Number 4
May 2013
Authors: Samir S. Shaikh, Pravin B. Landage, D. B. Kshirsagar
74e038d7-dd14-4f59-8871-7e38c2e847e5

Samir S. Shaikh, Pravin B. Landage, D. B. Kshirsagar . User Navigation Pattern Prediction using Longest Common Subsequence. International Conference on Recent Trends in Engineering and Technology 2013. ICRTET, 4 (May 2013), 8-11.

@article{
author = { Samir S. Shaikh, Pravin B. Landage, D. B. Kshirsagar },
title = { User Navigation Pattern Prediction using Longest Common Subsequence },
journal = { International Conference on Recent Trends in Engineering and Technology 2013 },
issue_date = { May 2013 },
volume = { ICRTET },
number = { 4 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 8-11 },
numpages = 4,
url = { /proceedings/icrtet/number4/11784-1341/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Engineering and Technology 2013
%A Samir S. Shaikh
%A Pravin B. Landage
%A D. B. Kshirsagar
%T User Navigation Pattern Prediction using Longest Common Subsequence
%J International Conference on Recent Trends in Engineering and Technology 2013
%@ 0975-8887
%V ICRTET
%N 4
%P 8-11
%D 2013
%I International Journal of Computer Applications
Abstract

Web mining applies the data mining, the artificial intelligence and the chart technology and so on to the web data and traces users' visiting characteristics, and then extracts the users' using pattern. Web mining technologies are the right solutions for knowledge discovery on the Web. The knowledge extracted from the Web can be used to raise the performances for Web information retrievals, question answering, and Web based data warehousing. In this paper, I provide an introduction of Web mining as well as a review of the Web mining categories. Web mining applies the data mining, the artificial intelligence and the chart technology and so on to the web data. And traces users' visiting characteristics, and then extracts the users' navigation pattern. Web mining has quickly become one of the most important areas in Computer and Information Sciences because of its direct applications in ecommerce, e-CRM, Web analytics, information retrieval and filtering, and Web information systems.

References
  1. V. Sujatha, Punithavalli, "Improved user navigation pattern prediction technique from web log data", International Conference on Communication Technology and System Design, 2011, Elsevier publication, Procedia Engineering 30 (2012) pp. 92 – 99
  2. Yue-Shi Lee, Show-Jane Yen, "Incremental and interactive mining of web traversal patterns", 2008, Elsevier publication, Information Sciences 178 (2008) pp. 287–306.
  3. Neetu Anand, Saba Hilal, "Identifying the User Access Pattern in Web Log Data", International Journal of Computer Science and Information Technologies, Vol. 3 (2) , 2012, pp. 3536-3539
  4. Yan Wang, "Web Mining and Knowledge Discovery of Usage Patterns" Google Documents, 2000.
  5. Oren Etzioni, "The world wide Web: Quagmire or gold mine", Communications of the ACM, 39(11), 1996, pp. 65-68
  6. Kumar, P. R. and Singh, A. K. , "Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval", American Journal of Applied Sciences, 2010, Vol. 7, No. 6, Pp. 840-845.
  7. Ranieri Baraglia, Paolo Palmerini, "SUGGEST : AWeb Usage Mining System", Proc. of IEEE International Conference on Information Technology: Coding and Computing, 2004.
  8. Haibin Liu, Vlado Kes?elj, "Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users' future requests", Data & Knowledge Engineering 61 2007, Elsevier publication, pp. 304–330.
  9. Bamshad Mobasher, Robert Cooley, and Jaideep Srivastava, "Automatic personalization based on web usage mining", Communication of ACM Vol. 43 No. 8, 2000, pp. 142-151.
  10. Mehrdad Jalali, Norwati Mustapha, Md Nasir Sulaiman, Ali Mamat, "A Recommender System Approach for Classifying User Navigation Patterns Using Longest Common Subsequence Algorithm", American Journal of Scientific Research, ISSN 1450-223X Issue 4 (2009), pp. 17-27.
  11. Dipa Dixit, Jayant Gadage, "New Approach for Clustering of Navigation Patterns of Online Users", International Journal of Engineering Science and Technology, Vol. 2(6), 2010, pp. 1670-1676.
Index Terms

Computer Science
Information Sciences

Keywords

Web Usage Mining Longest Common Subsequence Graph Partitioning Navigation Pattern