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A fine grained heuristic to capture web navigation patterns

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Published:01 June 2000Publication History
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References

  1. {1} J. Borges and M. Levene. Mining association rules in hypertext databases. In Proc. of the fourth International Conference on Knowledge Discovery and Data Mining, pages 149-153, New York, USA, August 1998.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. {2} J. Borges and M. Levene. Data mining of user navigation patterns. In Proc. of the Web Usage Analysis and User Profiling Workshop, pages 31-36, San Diego, California, August 1999.Google ScholarGoogle Scholar
  3. {3} J. Borges and M. Levene. A heuristic to capture longer user web navigation patterns. In Proc. of the first International Conference on Electronic Commerce and Web Technologies, Greenwich, U.K., September 2000. To appear.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. {4} I. Cadez, S. Gaffney, and P. Smyth. A general probabilistic framework for clustering individuals. In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, August 2000. To appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. {5} I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White. Visualization of navigation patterns on a web site using model based clustering. In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, August 2000. To appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. {6} L. D. Catledge and J. E. Pitkow. Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems, 27(6):1065-1073, April 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. {7} E. Charniak. Statistical Language Learning. The MIT Press, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. {8} C. Chatfield. Statistical inferences regarding markov chain models. Applied Statistics, 22:7-20, 1973.Google ScholarGoogle ScholarCross RefCross Ref
  9. {9} M.-S. Chen, J. S. Park, and P. S. Yu. Efficient data mining for traversal patterns. IEEE Transactions on Knowledge and Data Engineering, 10(2):209-221, March/April 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. {10} J. Conklin. Hypertext: An introduction and survey. IEEE Computer, 20(9):17-41, September 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. {11} R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1(1):5-32, February 1999.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. {12} M. Levene and G. Loizou. A probabilistic approach to navigation in hypertext. Information Sciences, 114:165-186, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. {13} J. Nielsen. The art of navigating through hypertext. Communications of the ACM, 33(3):296-310, March 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. {14} M. Perkowltz and O. Etzioni. Adaptive web sites: an AI challenge. In Proc. of fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 16-21, Nagoya, Japan, August 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. {15} M. Perkowitz and O. Etzioni. Adaptive sites: Automatically synthesizing web pages. In Proe. of the fifteenth National Conference on Artificial Intelligence (AAAI-98), pages 727- 732, Madison, Wisconsin, July 1998. Google ScholarGoogle Scholar
  16. {16} R. R. Sarukkai. Link prediction and path analysis using Markov chains. In Proceedings of the ninth International World Wide Web Conference, Amsterdam, Holland, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. {17} S. Schechter, M. Krishnan, and M. D. Smith. Using path profiles to predict http requests. Computer Networks and ISDN Systems, 30:457-467, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. {18} M. Spiliopoulou and L. C. Faulstich. WUM: a tool for web utilization analysis. In Proc. of the International Workshop on the Web and Databases (WebDB'98), pages 184-203, Valencia, Spain, March 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. {19} 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):12-23, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. {20} R. Stout. Web Site Stats: tracking hits and analyzing traffic. Osborne McGraw-Hill, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. {21} C. S. Wetherell. Probabilistic languages: A review and some open questions. Computing Surveys, 12(4):361-379, December 1980. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. {22} T. W. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking. In Proc. of the fifth International World Wide Web Conference, pages 1007-1014, Paris, France, May 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. {23} N. Zin and M. Levene. Constructing web-views from automated navigation sessions. In Proc. of the ACM Digital Libraries Workshop on Organizing Web Space, pages 54-58, Berkeley, California, August 1999.Google ScholarGoogle Scholar

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