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A Framework for Mining Fuzzy Association Rules from Composite Items

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New Frontiers in Applied Data Mining (PAKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5433))

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Abstract

A novel framework is described for mining fuzzy Association Rules (ARs) relating the properties of composite attributes, i.e. attributes or items that each feature a number of values derived from a common schema. To apply fuzzy Association Rule Mining (ARM) we partition the property values into fuzzy property sets. This paper describes: (i) the process of deriving the fuzzy sets (Composite Fuzzy ARM or CFARM) and (ii) a unique property ARM algorithm founded on the correlation factor interestingness measure. The paper includes a complete analysis, demonstrating: (i) the potential of fuzzy property ARs, and (ii) that a more succinct set of property ARs (than that generated using a non-fuzzy method) can be produced using the proposed approach.

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Author information

Authors and Affiliations

  1. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M1 5GD, UK

    Maybin Muyeba

  2. Liverpool Hope University, Liverpool, L16 9JD, UK

    M. Sulaiman Khan

  3. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Frans Coenen

Authors
  1. Maybin Muyeba
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  2. M. Sulaiman Khan
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  3. Frans Coenen
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Editor information

Editors and Affiliations

  1. School of Information Technologies, University of Sydney, NSW, Australia

    Sanjay Chawla

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1, Mihogaoka, Ibaraki, 567, Osaka, Japan

    Takashi Washio

  3. Division of Computing Science, Hokkaido University, 060-0814, Sapporo, Japan

    Shin-ichi Minato

  4. School of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  5. Central Research Institute of Electric Power Industry, 2-11-1 Iwado-kita, Komae-shi, 201-8511, Tokyo, Japan

    Takashi Onoda

  6. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Seiji Yamada

  7. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, 567, Osaka, Japan

    Akihiro Inokuchi

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Muyeba, M., Khan, M.S., Coenen, F. (2009). A Framework for Mining Fuzzy Association Rules from Composite Items. In: Chawla, S., et al. New Frontiers in Applied Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00399-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-00399-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00398-1

  • Online ISBN: 978-3-642-00399-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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