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Reseach Article

Indirect Correlations: Significance In Web Mining

Published on May 2012 by Indu Singh, Gagandeep Kaur
National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
Foundation of Computer Science USA
RTMC - Number 3
May 2012
Authors: Indu Singh, Gagandeep Kaur
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Indu Singh, Gagandeep Kaur . Indirect Correlations: Significance In Web Mining. National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011. RTMC, 3 (May 2012), 36-40.

@article{
author = { Indu Singh, Gagandeep Kaur },
title = { Indirect Correlations: Significance In Web Mining },
journal = { National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011 },
issue_date = { May 2012 },
volume = { RTMC },
number = { 3 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 36-40 },
numpages = 5,
url = { /proceedings/rtmc/number3/6641-1024/ },
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 Indu Singh
%A Gagandeep Kaur
%T Indirect Correlations: Significance In Web Mining
%J National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%@ 0975-8887
%V RTMC
%N 3
%P 36-40
%D 2012
%I International Journal of Computer Applications
Abstract

"direct" association rules reflect relationships existing between items that relatively often co-occur in common transactions direct association rules are dedicated to describe the direct correlations among the items in a frequent item set, indirect association rules are dedicated to describe the indirect correlations between the two items in a infrequent item set. Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. When a pair of items, (A, B), which seldom. occur together in the same transaction, are highly dependent on the presence of another item set C, then pair (A, B) are said to be indirectly associated via C . In this paper indirect association rules and significance of web usage mining are explained. How associations rules are beneficial for web usage mining is explained.

References
  1. . Weimin Ouyang and Qinhua Huang, "Discovery Algorithm for Mining both Direct and Indirect Weighted Association Rules" In International Conference on Artificial Intelligence and Computational Intelligence, 2009, IEEE.
  2. . Jaideep Srivastava, Prasanna Desikan, Vipin Kumar, "Web Mining : Concepts and Applications & Rerearch Directions",in April,2009.
  3. . Przemyslaw Kazienko,"Mining Indirect Association Rules For Web Recommendation" Int. J. Appl. Math. Compt. Sci. , Vol. 19, No. 1, 165–186. 2009.
  4. Pang-Ning Tan and Vipin Kumar," Mining Indirect Associations in Web Data", in the proceeding of conference on WEBKDD in 2001, LNAI 2356, pp. 145–166, 2002.
  5. T. Yah, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking. In Fifth International World Wide Web Conference, Paris, France, 1996.
  6. Mike Perkowitz and Oren Etzioni. Adaptive web sites: Automatically synthesizing web pages. In Fifteenth National Conference on Artificial Intelligence, Madison, WI, 1998.
  7. Mike Perkowitz and Oren Etzioni. Adaptive web sites: Conceptual cluster mining. In Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999.
  8. L. Catledgeand J. Pitkow. Characterizing browsing behaviors on the world wide web. Computer Networksand ISDN Systems, 27(6), 1995.
  9. Chi E. H. , Pitkow J. , Mackinlay J. , Pirolli P. , Gossweiler, and Card S. K. Visualizing the evolution of web ecologies. In CHI '98, Los Angeles, California, 1998.
  10. R. Kosala and H. Blockeel, "Web Mining Research: A Survey,"ACM SIGKDD Explorations, vol. 2, no. 1, pp. 1–15. 2000.
  11. M. Spiliopoulou. Data mining for the web. In Principles of Data Mining and Knowledge Discovery, Second European Symposium, PKDD '99, pages 588-589, 1999.
  12. J. Srivastava, R. Cooley, M. Deshpande, and P. -N. Tan. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2), 2000.
  13. Borges and M. Levene. Data mining of user navigation patterns. In Proceedings of the WBBKDD'99 Workshop on Web Usage Analysis and User Profiling, August 15, 1999, San Diego, CA, USA, pages 31-36, 1999.
  14. B. Masand and M. Spiliopoulou. Webkdd-99: Workshop on web usage analysis and user profiling. SIGKDD Explorations, 1(2), 2000.
  15. J. Borges and M. Levene. "Mining association rules in hypertext databases". In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), August 27-31, 1998, New York City, New York, USA, 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Indirect Correlations