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Multi-objective Genetic Algorithm Based Method for Mining Optimized Fuzzy Association Rules

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Intelligent Data Engineering and Automated Learning – IDEAL 2004 (IDEAL 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3177))

Abstract

This paper introduces optimized fuzzy association rules mining. We propose a multi-objective Genetic Algorithm (GA) based approach for mining fuzzy association rules containing instantiated and uninstantiated attributes. According to our method, fuzzy association rules can contain an arbitrary number of uninstantiated attributes. The method uses three bjectives for the rule mining process: support, confidence and number of fuzzy sets. Experimental results conducted on a real data set demonstrate the effectiveness and applicability of the proposed approach.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Firat University, 23119, Elazig, Turkey

    Mehmet Kaya

  2. ADSA Lab & Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    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 Engineering, Computing, and Mathematics, University of Exeter, EX4 4QF, Exeter, UK

    Zheng Rong Yang

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. School of Engineering, Computer Science and Mathematics, University of Exeter, EX4 4QF, UK

    Richard M. Everson

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© 2004 Springer-Verlag Berlin Heidelberg

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Kaya, M., Alhajj, R. (2004). Multi-objective Genetic Algorithm Based Method for Mining Optimized Fuzzy Association Rules. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_113

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  • DOI: https://doi.org/10.1007/978-3-540-28651-6_113

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22881-3

  • Online ISBN: 978-3-540-28651-6

  • eBook Packages: Springer Book Archive

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