Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework

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

Abstract

In this paper we extend the problem of mining weighted association rules. A classical model of boolean and fuzzy quantitative association rule mining is adopted to 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 w.r.t some user defined criteria. Most works on DCP so far struggle with invalid downward closure property and some assumptions are made to validate the property. We generalize the problem of downward closure property and propose a fuzzy weighted support and confidence framework for boolean and quantitative items with weighted settings. The problem of invalidation of the DCP is solved using an improved model of weighted support and confidence framework for classical and fuzzy association rule mining. Our methodology follows an Apriori algorithm approach and avoids pre and post processing as opposed to most weighted ARM algorithms, thus eliminating the extra steps during rules generation. The paper concludes with experimental results and discussion on evaluating the proposed framework.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: New Frontiers in Applied Data Mining
• Publication Date: Feb 7, 2009
• Deposit Date: Oct 4, 2024
• Publisher: Springer
• Pages: 49-61
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743
• Book Title: New Frontiers in Applied Data Mining
• ISBN: 9783642003981
• DOI: https://doi.org/10.1007/978-3-642-00399-8_5
• Keywords: Association rules, fuzzy weighted support, weighted confidence, downward closure