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The Variable Precision Rough Set Model for Web Usage Mining

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Web Intelligence: Research and Development (WI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2198))

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Abstract

Web Knowledge Discovery and Data Mining includes discovery and leveraging different kinds of hidden patterns in webdata. In this paper we mine webuser access patterns and classify users using the Variable Precision Rough Set (VPRS) model. Certain user sessions of webaccess are positive examples and other sessions are negative examples. Cumulative graphs capture all known positive example sessions and negative example sessions. They are then used to identify the attributes that are used to form an equivalence relation. This equivalence relation is used for the ß-probabilistic approximation classification of the VPRS model. An illustrative experiment is presented.

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References

  1. R. Kosala and H. Blockeel. Webmining research: A survey. SIGKDD Explorations, 2(1), July 2000.

    Google Scholar 

  2. Z. Pawlak. Rough sets. International Journal of Computer and Information Sciences, 11(5):341–356, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  3. Z. Pawlak. Rough Sets-Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991.

    MATH  Google Scholar 

  4. W. Ziarko. Variable precision rough set model. Journal of Computer and System Sciences, 46(1):39–59, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  5. A. An, C. Chan, N. Shan, N. Cercone, and W. Ziarko. Applying knowledge discovery to predict water-supply consumption. IEEE Expert, 12(4):72–78, 1997.

    Article  Google Scholar 

  6. J. Borges and M. Levene. Mining association rules in hypertext databases. In Proc. Fourth Int. Conf. on Knowledge Discovery and Data Mining, pages 149–153, 1998.

    Google Scholar 

  7. V. Uma Maheswari, Arul Siromoney, and K. M. Mehata. Mining webusage graphs. In Knowledge Based Computer Systems (KBCS2000), pages 186–192. Allied Publishers Limited, 2000.

    Google Scholar 

  8. M. Perkowitz and O. Etzioni. Towards adaptive websites: Conceptual framework and case study. In The Eighth Int. World Wide WebConfer ence, Toronto, Canada, May 1999.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Anna University, Chennai, 600 025, India

    V. Uma Maheswari, Arul Siromoney & K. M. Mehata

Authors
  1. V. Uma Maheswari
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  2. Arul Siromoney
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  3. K. M. Mehata
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Editor information

Editors and Affiliations

  1. Department of Systems and Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816, Japan

    Ning Zhong

  2. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    Yiju Yao

  3. Department of Computer Science, Hong Kong Baptist University, 224 Waterloo Road, Kowloon, Hong Kong, China

    Jiming Liu

  4. Department of Information and Computer Science, Waseda University, 3-4-1 Okubo Shinjuku-Ku, Tokyo, 169, Japan

    Setsuo Ohsuga

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© 2001 Springer-Verlag Berlin Heidelberg

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Maheswari, V.U., Siromoney, A., Mehata, K.M. (2001). The Variable Precision Rough Set Model for Web Usage Mining. In: Zhong, N., Yao, Y., Liu, J., Ohsuga, S. (eds) Web Intelligence: Research and Development. WI 2001. Lecture Notes in Computer Science(), vol 2198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45490-X_67

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  • DOI: https://doi.org/10.1007/3-540-45490-X_67

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42730-8

  • Online ISBN: 978-3-540-45490-8

  • eBook Packages: Springer Book Archive

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