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Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach

Basheer Mohamad Al-Maqaleh Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
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  1. Basheer Mohamad Al-Maqaleh. Article: Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach. International Journal of Applied Information Systems 5(3):47-52, February 2013. BibTeX

    	author = "Basheer Mohamad Al-Maqaleh",
    	title = "Article: Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 3,
    	pages = "47-52",
    	month = "February",
    	note = "Published by Foundation of Computer Science, New York, USA"


Association rule mining is considered as one of the important tasks of data mining intended towards decision making process. It has been mainly developed to identify interesting associations and/or correlation relationships between frequent itemsets in datasets. A multi-objective genetic algorithm approach is proposed in this paper for the discovery of interesting association rules with multiple criteria i. e. support, confidence and simplicity (comprehensibility). With Genetic Algorithm (GA), a global search can be achieved and system automation is developed, because the proposed algorithm could identify interesting association rules from a dataset without having the user-specified thresholds of minimum support and minimum confidence. The experimental results on various types of datasets show the usefulness and effectiveness of the proposed algorithm.


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Association Rule, Interestingness Measure, Genetic Operators, Frequent Itemsets