Optimized Association Rule Mining with Genetic Algorithms
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Date
2011
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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. Keywords:
optimal association rules, genetic algorithms, multi–objective
interestingness metrics
Description
Keywords
Genetic Algorithms, Association, Rule Mining
Citation
Wakabi-Waiswa, P. P., Baryamureeba, V., & Sarukesi, K. (2011, July). Optimized association rule mining with genetic algorithms. In 2011 Seventh International Conference on Natural Computation (Vol. 2, pp. 1116-1120). IEEE. ISBN 978-1-4244-9953