Effective Mining of Fuzzy Multi-Cross-Level Weighted Association Rules

  • Mehmet Kaya
  • Reda Alhajj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


This paper addresses fuzzy weighted multi-cross-level association rule mining. We define a fuzzy data cube, which facilitates for handling quantitative values of dimensional attributes, and hence allows for mining fuzzy association rules at different levels. A method is introduced for single dimension fuzzy weighted association rules mining. To the best of our knowledge, none of the studies described in the literature considers weighting the internal nodes in such taxonomy. Only items appearing in transactions are weighted to find more specific and important knowledge. But, sometimes weighting internal nodes on a tree may be more meaningful and enough. We compared the proposed approach to an existing approach that does not utilize fuzziness. The reported experimental results demonstrate the effectiveness and applicability of the proposed fuzzy weighted multi-cross-level mining approach.


Association Rule Minimum Support Association Rule Mining Data Cube Fuzzy Association Rule 
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

  • Mehmet Kaya
    • 1
  • Reda Alhajj
    • 2
    • 3
  1. 1.Dept. of Computer EngFirat UniversityElazigTurkey
  2. 2.Dept. of Computer ScienceUniversity of CalgaryCalgaryCanada
  3. 3.Dept. of Computer ScienceGlobal UniversityBeirutLebanon

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