A framework for mining fuzzy association rules from composite items

Muyeba, Maybin; Sulaiman Khan, M.; Coenen, Frans

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: PAKDD 2008 International Workshops
• Start Date: May 20, 2008
• Publication Date: 2009
• Deposit Date: Apr 7, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 5433
• Book Title: New Frontiers in Applied Data Mining
• ISBN: 978-3-642-00398-1
• DOI: https://doi.org/10.1007/978-3-642-00399-8_6