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Discovery of Interesting Usage Patterns from Web Data

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Web Usage Analysis and User Profiling (WebKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1836))

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Abstract

Web Usage Mining is the application of data mining techniques to large Web data repositories in order to extract usage patterns. As with many data mining application domains, the identification of patterns that are considered interesting is a problem that must be solved in addition to simply generating them. Aneces sary step in identifying interesting results is quantifying what is considered uninteresting in order to form a basis for comparison. Several research efforts have relied on manually generated sets of uninteresting rules. However, manual generation of a comprehensive set of evidence about beliefs for a particular domain is impractical in many cases. Generally, domain knowledge can be used to automatically create evidence for or against a set of beliefs. This paper develops a quantitative model based on support logic for determining the interestingness of discovered patterns. For Web Usage Mining, there are three types of domain information available; usage, content, and structure. This paper also describes algorithms for using these three types of information to automatically identify interesting knowledge. These algorithms have been incorporated into the Web Site Information Filter (WebSIFT) system and examples of interesting frequent itemsets automatically discovered from real Web data are presented.

Supported by NSF grant EHR-9554517

Supported by ARL contract DA/DAKF11-98-P-0359

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Minnesota, USA

    Robert Cooley, Pang-Ning Tan & Jaideep Srivastava

Authors
  1. Robert Cooley
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  2. Pang-Ning Tan
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  3. Jaideep Srivastava
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Editor information

Editors and Affiliations

  1. Discovery and Intelligent Agents Technology, Redwood Investment Systems Inc., Boston, MA, 02110-1225, USA

    Brij Masand (Director of Knowledge) (Director of Knowledge)

  2. Institut für Wirtschaftsinformatik, Humboldt Universität zu Berlin, Spandauer Str. 1, 10178, Berlin, Germany

    Myra Spiliopoulou

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Cooley, R., Tan, PN., Srivastava, J. (2000). Discovery of Interesting Usage Patterns from Web Data. In: Masand, B., Spiliopoulou, M. (eds) Web Usage Analysis and User Profiling. WebKDD 1999. Lecture Notes in Computer Science(), vol 1836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44934-5_10

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  • DOI: https://doi.org/10.1007/3-540-44934-5_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67818-2

  • Online ISBN: 978-3-540-44934-8

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