Mining fuzzy association rules from composite items

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

Abstract

This paper presents an approach for mining fuzzy Association Rules (ARs) relating the properties of composite items, i.e. items that each feature a number of values derived from a common schema. We partition the values associated to properties into fuzzy sets in order to apply fuzzy Association Rule Mining (ARM). This paper describes the process of deriving the fuzzy sets from the properties associated to composite items and a unique Composite Fuzzy Association Rule Mining (CFARM) algorithm founded on the certainty factor interestingness measure to extract fuzzy association rules. The paper demonstrates the potential of composite fuzzy property ARs, and that a more succinct set of property ARs can be produced using the proposed approach than that generated using a non-fuzzy method.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IFIP 20th World Computer Congress, TC 12: IFIP AI 2008 Stream
• Start Date: Sep 7, 2008
• End Date: Sep 10, 2008
• Publication Date: 2008
• Deposit Date: Apr 7, 2025
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Series Title: IFIP Advances in Information and Communication Technology
• Series Number: 276
• Book Title: Artificial Intelligence in Theory and Practice II
• ISBN: 978-3-642-00398-1
• DOI: https://doi.org/10.1007/978-0-387-09695-7_7