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Effect of Similar Behaving Attributes in Mining of Fuzzy Association Rules in the Large Databases

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3980))

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Abstract

Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback. It often produces a huge number of fuzzy associations. This is particularly true for datasets whose attributes are highly correlated. The huge number of fuzzy associations makes it very difficult for a human user to analyze them. Existing research has shown that most of the discovered rules are actually redundant or insignificant. In this paper, we propose a novel technique to overcome this problem.The approach is effective because experiment results show that the set of produced rules is typically very small. Our solution also reduces the size of average transactions and dataset. Our performance study shows that this solution has a superior performance over the other algorithms.

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Author information

Authors and Affiliations

  1. SECOMP Lab., Department of Computer & IT, Iran University of Science & Technology (IUST), Tehran, Iran

    Zahra Farzanyar

  2. Department of Computer & IT, Iran University of Science & Technology (IUST), Tehran, Iran

    Mohammadreza Kangavari & Sattar Hashemi

Authors
  1. Zahra Farzanyar
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  2. Mohammadreza Kangavari
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  3. Sattar Hashemi
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Farzanyar, Z., Kangavari, M., Hashemi, S. (2006). Effect of Similar Behaving Attributes in Mining of Fuzzy Association Rules in the Large Databases. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_120

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  • DOI: https://doi.org/10.1007/11751540_120

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34070-6

  • Online ISBN: 978-3-540-34071-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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