A Framework for Mining Fuzzy Association Rules from Composite Items

Authors:
Maybin Muyeba;M. Sulaiman Khan;Frans Coenen
Affiliations:
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK M1 5GD;Liverpool Hope University, Liverpool, UK L16 9JD;Department of Computer Science, University of Liverpool, Liverpool, UK L69 3BX
Venue:
New Frontiers in Applied Data Mining
Year:
2009

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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.