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Mining Indirect Associations in Web Data

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WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points (WebKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2356))

Abstract

Web associations are valuable patterns because they provide useful insights into the browsing behavior of Web users. However, there are two major drawbacks of using current techniques for mining Web association patterns, namely, their inability to detect interesting negative associations in data and their failure to account for the impact of site structure on the support of a pattern. To address these issues, a new data mining technique called indirect association is applied to the Web click-stream data. The idea here is to find pairs of pages that are negatively associated with each other, but are positively associated with another set of pages called the mediator. These pairs of pages are said to be indirectly associated via their common mediator. Indirect associations are interesting patterns because they represent the diverse interests of Web users who share a similar traversal path. These patterns are not easily found using existing data mining techniques unless the groups of users are known a priori. The effectiveness of indirect association is demonstrated using Web data from an academic institution and an online Web store.

This work was partially supported by NSF grant # ACI-9982274 and by Army High Performance Computing Research Center contract number DAAD19-01-2-0014. The content of this work does not necessarily reflect the position or policy of the government and no official endorsement should be inferred. Access to computing facilities was provided by AHPCRC and the Minnesota Supercomputing Institute.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Minnesota, Minneapolis, MN, 55455

    Pang-Ning Tan & Vipin Kumar

Authors
  1. Pang-Ning Tan
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  2. Vipin Kumar
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Editor information

Editors and Affiliations

  1. Blue Martini Software, 2600 Campus Drive, San Mateo, CA, 94403, USA

    Ron Kohavi

  2. Data Miners Inc., 77 North Washington Street, Boston, MA, 02114, USA

    Brij M. Masand

  3. Leipzig Graduate School of Management, Jahnallee 59, 04109, Leipzig, Germany

    Myra Spiliopoulou

  4. University of Minnesota, 4-192 EECS Building 200 Union St SE, Minneapolis, MN, 55455

    Jaideep Srivastava

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© 2002 Springer-Verlag Berlin Heidelberg

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Tan, PN., Kumar, V. (2002). Mining Indirect Associations in Web Data. In: Kohavi, R., Masand, B.M., Spiliopoulou, M., Srivastava, J. (eds) WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points. WebKDD 2001. Lecture Notes in Computer Science(), vol 2356. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45640-6_7

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  • DOI: https://doi.org/10.1007/3-540-45640-6_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43969-1

  • Online ISBN: 978-3-540-45640-7

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