Effective mining of weighted fuzzy association rules

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

Abstract

A novel approach is presented for effectively mining weighted fuzzy association rules (ARs). The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance wrt some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. This chapter generalizes the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. The authors’ approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The chapter presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach.

• Publication Date: 2009
• Deposit Date: Apr 11, 2025
• Publisher: IGI Global
• Peer Reviewed: Peer Reviewed
• Pages: 18
• Book Title: Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection
• DOI: https://doi.org/10.4018/978-1-60566-754-6.ch004