Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework

Authors:
Maybin Muyeba;M. Sulaiman Khan;Frans Coenen
Affiliations:
Dept. of Computing, Manchester Metropolitan University, Manchester, UK M1 5GD;School of Computing, Liverpool Hope University, Liverpool, UK L16 9JD;Dept. of Computer Science, University of Liverpool, Liverpool, UK L69 3BX
Venue:
New Frontiers in Applied Data Mining
Year:
2009

Citing 9
Cited 1

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases

ACM SIGMOD Record
Efficient mining of weighted association rules (WAR)

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Weighted Association Rules for Fuzzy Quantitative Items

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Association Rules with Weighted Items

IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Tree Structures for Mining Association Rules

Data Mining and Knowledge Discovery
Weighted Association Rule Mining using weighted support and significance framework

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining weighted association rules

Intelligent Data Analysis

A novel unsupervised fuzzy clustering method for preprocessing of quantitative attributes in association rule mining

Information Technology and Management

Quantified Score

Hi-index	0.00

Visualization

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.