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Reseach Article

Web Usage Mining Systems and Technologies

Published on May 2012 by Sushila Gauthwal
National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
Foundation of Computer Science USA
RTMC - Number 5
May 2012
Authors: Sushila Gauthwal
ba3c04a8-9533-4f96-9d55-650afd658d15

Sushila Gauthwal . Web Usage Mining Systems and Technologies. National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011. RTMC, 5 (May 2012), 16-20.

@article{
author = { Sushila Gauthwal },
title = { Web Usage Mining Systems and Technologies },
journal = { National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011 },
issue_date = { May 2012 },
volume = { RTMC },
number = { 5 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /proceedings/rtmc/number5/6653-1036/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%A Sushila Gauthwal
%T Web Usage Mining Systems and Technologies
%J National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%@ 0975-8887
%V RTMC
%N 5
%P 16-20
%D 2012
%I International Journal of Computer Applications
Abstract

Web usage mining is the area of data mining which deals with the discovery and analysis of usage patterns from Web data, specifically web logs, in order to improve web based applications. Web usage mining is used to discover interesting user navigation patterns and can be applied to many real-world problems, such as improving Web sites/pages, making additional topic or product recommendations, user/customer behaviour studies, etc. This article provides a survey and analysis of current Web usage mining systems and technologies. A Web usage mining system performs five major tasks: i) data gathering, ii) data preparation, iii) navigation pattern discovery, iv) pattern analysis and visualization, and v) pattern applications. Each task is explained in detail and its related technologies are introduced. A list of major research systems and projects concerning Web usage mining is also presented, and a summary of Web usage mining is given in the last section

References
  1. Access log analyzers. Retrieved June 02, 2003 from http://www. uu. se/Software/Analyzers/Accessanalyzers. html
  2. Gediminas Adomavicius and Alexander Tuzhilin. Using data mining methods to build customer profiles.
  3. IEEE Computer, 34(2):74-82, February 200] Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Proceeding of the 20th Very Large DataBases Conference (VLDB),pages 487-499, Santiago, Chile, 1994.
  4. Rakesh Agrawal and Ramakrishnan Srikant. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering, pages 3-14,Taipei, Taiwan, March 1995.
  5. José Borges and Mark Levene. Data mining of user navigation patterns. In Proceedings of the Workshop on Web Usage Analysis and User Profiling (WEBKDD),pages 31-36, San Diego, California, August 1999.
  6. Alex G. Büchner, Matthias Baumgarten, Sarabjot S. Anand, Maurice D. Mulvenna, and John G. Hughes. Navigation pattern discovery from Internet data. In Proceedings of the Workshop on Web Usage Analysis and User Profiling (WEBKDD), San Diego, California, August 1999.
  7. Alex G. Büchner and Maurice D. Mulvenna. Discovering Internet marketing intelligence through online analytical Web usage mining. ACM SIGMOD Record,27(4):54-61, December 1998.
  8. CGI environment variables. Retrieved May 15, 2003 From http://hoohoo. ncsa. uiuc. edu/cgi/env. html
  9. Ming-Syan Chen, Jong Soo Park, and Philip S. Yu. Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering,8(6):866-883, 1996.
  10. Common log file format. Retrieved June 02, 2003 from http://www. w3. org/Daemon/User/Config/Logging. html 58 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 1 - NUMBER 4
  11. Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava. Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems, 1(1):5-32, February 1999.
  12. Bin Lan, Stephane Bressan, and Beng Chin Ooi. Making Web servers pushier. In Proceedings of the Workshop on Web Usage Analysis and User Profiling, pages 112-125, San Diego, California, August 1999.
  13. Myra Spiliopoulou and Lukas C. Faulstich. WUM: A tool for Web utilization analysis. In Proceedings of the Workshop on the Web and Databases (WEBDB),pages 184-203, Valencia, Spain, March 1998.
  14. Extended log file format. Retrieved June 03, 2003 from http://www. w3. org/TR/WD-logfile. html
  15. Yongjian Fu, Kanwalpreet Sandhu, and Ming-Yi Shih. A generalization-based approach to clustering of Web usage sessions. In Brij M. Masand and Myra Spiliopoulou, editors, Web Usage Analysis and User Profiling,Lecture Notes in Artificial Intelligence, 1836:21-38,Springer, 2000.
  16. GroupLens Research. Retrieved May 12, 2003 from http://www. cs. umn. edu/Research/GroupLens/
  17. Jason I. Hong and James A. Landay. WebQuilt: A framework for capturing and visualizing the Web experience. In Proceedings of the 10th International World Wide Web Conference, pages 717-724, HongKong, 2001.
  18. Wen-Chen Hu, Xuli Zong, Hung-Ju Chu, and Jui-Fa Chen. Usage mining for the World Wide Web. In Proceedings of the 6th World Multi-Conference on Systemics,Cybernetics and Informatics (SCI), pages 75-80,Orlando, Florida, July 14-18, 2002.
  19. Melody Y. Ivory and Marti A. Hearst. Improving Web site design. IEEE Internet Computing, 6(2):56-63,March/April 2002.
  20. Raymond Kosala and Hendrik Blockeel. Web mining research: A survey. SIGKDD Explorations, 2(1):1-15,2000.
  21. Jaideep Srivastava, Robert Cooley, Mukund Deshpande,and Pang-Ning Tan. Web usage mining: Discovery and applications of usage patterns from Web data. ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Explorations, 1(2):12-23,2000.
Index Terms

Computer Science
Information Sciences

Keywords

World Wide Web Usage Mining Navigation Patterns Usage Data And Data Mining