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Finding Associations in Composite Data Sets: The CFARM Algorithm

Author

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  • M. Sulaiman Khan

    (University of Liverpool, UK)

  • Maybin Muyeba

    (Manchester Metropolitan University, UK)

  • Frans Coenen

    (University of Liverpool, UK)

  • David Reid

    (Liverpool Hope University, UK)

  • Hissam Tawfik

    (Liverpool Hope University, UK)

Abstract

In this paper, a composite fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.

Suggested Citation

  • M. Sulaiman Khan & Maybin Muyeba & Frans Coenen & David Reid & Hissam Tawfik, 2011. "Finding Associations in Composite Data Sets: The CFARM Algorithm," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 7(3), pages 1-29, July.
    • Handle: RePEc:igg:jdwm00:v:7:y:2011:i:3:p:1-29
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