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A Framework for Mining Fuzzy Association Rules from Composite Items

  • Maybin Muyeba
  • M. Sulaiman Khan
  • Frans Coenen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5433)

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.

Keywords

Association rules fuzzy association rules composite attributes quantitative attributes 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maybin Muyeba
    • 1
  • M. Sulaiman Khan
    • 2
  • Frans Coenen
    • 3
  1. 1.Department of Computing and MathematicsManchester Metropolitan UniversityManchesterUK
  2. 2.Liverpool Hope UniversityLiverpoolUK
  3. 3.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK

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