NDLI: Optimized Association Rule Mining with genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Wakabi-Waiswa, P.P. Baryamureeba, V. Sarukesi, K.
Copyright Year	2011
Description	Author affiliation: Hindustan University, Chennai - 603 103, India (Sarukesi, K.) \|\| Makerere University, Kampala, Uganda (Wakabi-Waiswa, P.P.; Baryamureeba, V.)
Abstract	The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most ‘interesting’ or ‘optimal’ rules in the dataset. In this paper we propose a multi-objective approach to generating optimal association rules using two new rule quality metrics: syntactic superiority and transactional superiority. These two metrics ensure that dominated but interesting rules are returned to not eliminated from the resulting set of rules. Experimental results show that when we modify the dominance relations new interesting rules emerge implying that when dominance is solely determined through the raw objective values there is a high chance of eliminating interesting rules.
Starting Page	1116
Ending Page	1120
File Size	211441
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Databases multi-objective interestingness metrics Syntactics optimal association rules genetic algorithms Association rules Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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