Extending OLAP with Fuzziness for Effective Mining of Fuzzy Multidimensional Weighted Association Rules

  • Mehmet Kaya
  • Reda Alhajj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


This paper contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube combined with the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach.


association rules data mining fuzzy data cube multidimensional mining OLAP weighted mining 


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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 UniversityBeirut

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