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Using an Interest Ontology for Improved Support in Rule Mining

  • Xiaoming Chen
  • Xuan Zhou
  • Richard Scherl
  • James Geller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, there are two problems. Some data sets are too sparse for coming up with rules with high support. Secondly, some data sets with abstract interests do not represent the actual interests well. To overcome these problems, we are preprocessing the data tuples using an ontology of interests. Thus, interests within tuples that are very specific are replaced by more general interests retrieved from the interest ontology. This results in many more tuples at a more general level. Feeding those tuples to an association rule miner results in rules that have better support and that better represent the reality.

Keywords

Data Mining Association Rule Rule Mining Association Rule Mining Concept Hierarchy 
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

  • Xiaoming Chen
    • 1
  • Xuan Zhou
    • 1
  • Richard Scherl
    • 2
  • James Geller
    • 1
  1. 1.CS Dept.New Jersey Institute of TechnologyNewarkUSA
  2. 2.West Long BranchMonmouth University

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