A Heuristic to Capture Longer User Web Navigation Patterns

  • José Borges
  • Mark Levene
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1875)


In previous work we have proposed a data mining model to capture user web navigation patterns, which models the navigation sessions as a hypertext probabilistic grammar. The grammar’s higher probability strings correspond to the user preferred trails and an algorithm was given to find all strings with probability above a threshold. Herein, we propose a heuristic aimed at finding longer trails composed of links whose average probability is above the threshold. A dynamic threshold is provided whose value is at all times proportional to the length of the trail being evaluated. We report on experiments with both real and synthetic data which were conducted to assess the heuristic’s utility.


Synthetic Data Mining Association Rule Dynamic Threshold Exploration Tree Navigation Pattern 
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 2000

Authors and Affiliations

  • José Borges
    • 1
  • Mark Levene
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

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