Mining Significant Usage Patterns from Clickstream Data

  • Lin Lu
  • Margaret Dunham
  • Yu Meng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4198)


Discovery of usage patterns from Web data is one of the primary purposes for Web Usage Mining. In this paper, a technique to generate Significant Usage Patterns (SUP) is proposed and used to acquire significant “user preferred navigational trails”. The technique uses pipelined processing phases including sub-abstraction of sessionized Web clickstreams, clustering of the abstracted Web sessions, concept-based abstraction of the clustered sessions, and SUP generation. Using this technique, valuable customer behavior information can be extracted by Web site practitioners. Experiments conducted using Web log data provided by J.C.Penney demonstrate that SUPs of different types of customers are distinguishable and interpretable. This technique is particularly suited for analysis of dynamic websites.


User Session Longe Common Subsequence Abstraction Hierarchy General Page Clickstream Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lin Lu
    • 1
  • Margaret Dunham
    • 1
  • Yu Meng
    • 1
  1. 1.Department of Computer Science and EngineeringSouthern Methodist UniversityDallasUSA

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