Paper Info
Title
Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework
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.
Year
DOI
Venue
2008
10.1007/978-3-642-00399-8_5
PAKDD Workshops
Keywords
Field
DocType
association rule mining,association rule
Downward closure,Data mining,Fuzzy association rule mining,Computer science,Apriori algorithm,Fuzzy logic,Association rule learning,Boolean algebra,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
5433
0302-9743
6
PageRank 
References 
Authors
0.59
12
3
Name
Order
Citations
PageRank
Maybin K. Muyeba1477.61
M. Sulaiman Khan2686.77
Frans Coenen31283131.80