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Online Mining of Weighted Fuzzy Association Rules

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Book cover Computer and Information Sciences - ISCIS 2003 (ISCIS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2869))

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Abstract

Mining useful information and helpful knowledge from data transactions is evolving as an important research area. Current online techniques for mining association rules identify the relationship among transactions using binary values. However, transactions with quantitative values are commonly encountered in real-life applications. In this paper, we address this problem by introducing a fuzzy adjacency lattice, and then integrate the lattice structure with linguistic weights in a way to reflect the importance of items. Experiments conducted using synthetic data show the effectiveness of the proposed method for online generation of weighted fuzzy association rules.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Firat University, Elazig, TURKEY

    Mehmet Kaya

  2. Department of Computer Science, University of Calgary, Calgary, AB, CANADA

    Reda Alhajj

Authors
  1. Mehmet Kaya
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  2. Reda Alhajj
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Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Adnan Yazıcı

  2. Department of Computer Engineering, Middle East Technical University, 06531, Ankara, Turkey

    Cevat Şener

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Kaya, M., Alhajj, R. (2003). Online Mining of Weighted Fuzzy Association Rules. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_39

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  • DOI: https://doi.org/10.1007/978-3-540-39737-3_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20409-1

  • Online ISBN: 978-3-540-39737-3

  • eBook Packages: Springer Book Archive

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