A weighted utility framework for mining association rules

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

Abstract

Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of occurrence in a record i.e. their weight and utility is the same (weight=1 and utility=1) which is not always the case. However, items are actually different in many aspects in a number of real applications such as retail marketing, nutritional pattern mining etc. These differences between items may have a strong impact on decision making in many application unlike the use of standard ARM. Our framework, weighted utility ARM (WUARM), considers the varied significance and different frequency values of individual items as their weights and utilities. Thus, weighted utility mining focuses on identifying the itemsets with weighted utilities higher than the user specified weighted utility threshold. We conduct experiments on synthetic and real data sets using standard ARM, weighted ARM and weighted utility ARM (WUARM) and present analysis of the results

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 2008 Second UKSIM European Symposium on Computer Modeling and Simulation (EMS)
• Start Date: Sep 8, 2008
• End Date: Sep 10, 2008
• Publication Date: 2008
• Deposit Date: Apr 2, 2025
• Journal: Proceedings - EMS 2008, European Modelling Symposium, 2nd UKSim European Symposium on Computer Modelling and Simulation
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 87-92
• Book Title: 2008 Second UKSIM European Symposium on Computer Modeling and Simulation
• DOI: https://doi.org/10.1109/EMS.2008.73
• Keywords: Itemsets , Association rules , Data mining , Databases , Algorithm design and analysis , Object oriented modeling , Computational modeling