Discovery of Indirect Associations from Web Usage Data

  • Pang-Ning Tan
  • Vipin Kumar


Web associations are valuable patterns because they provide useful insights into the browsing behavior of Web users. However, there are several limitations in applying current techniques for mining association patterns in Web usage data. First, as current techniques rely on the support measure to eliminate infrequent patterns, they are unable to detect interesting negative associations in data. In addition, they do not account for the impact of Web site structure on the support of a pattern. To address these limitations, we describe the use of a new data mining technique called indirect association to discover interesting negative associations in Web click-stream data. The key idea behind indirect association is to find pairs of pages that are negatively associated with each other, but are often accessed together with a common set of pages called the mediator. Indirect associations are useful patterns because they can capture the different interests of Web users who share a common traversal path. This type of pattern is not easily found using conventional data mining techniques unless the groups of users are known a priori. A novel technique is also developed for merging indirect associations into more compact patterns. The effectiveness of mining indirect associations is demonstrated using Web data from an academic institution and an online Web store.


Association Rule Frequent Itemsets Indirect Association Frequent Sequence Outgoing Link 
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 2003

Authors and Affiliations

  • Pang-Ning Tan
    • 1
  • Vipin Kumar
    • 1
  1. 1.AHPCRC/University of MinnesotaMinneapolisUSA

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