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

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Advanced Data Mining and Applications (ADMA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4093))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Dept. of Computer Eng, Firat University, Elazig, Turkey

    Mehmet Kaya

  2. Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  3. Dept. of Computer Science, Global University, Beirut, Lebanon

    Reda Alhajj

Authors
  1. Mehmet Kaya
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  2. Reda Alhajj
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electronic Engineering, The University of Queensland, Queensland, Australia

    Xue Li

  2. University of Alberta, Canada

    Osmar R. Zaïane

  3. Northwest Polytechnical University, China

    Zhanhuai Li

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© 2006 Springer-Verlag Berlin Heidelberg

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Kaya, M., Alhajj, R. (2006). Extending OLAP with Fuzziness for Effective Mining of Fuzzy Multidimensional Weighted Association Rules. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_6

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  • DOI: https://doi.org/10.1007/11811305_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37025-3

  • Online ISBN: 978-3-540-37026-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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