ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

• Published in: Knowledge-based systems Vol. 154; pp. 68 - 80
• Main Authors: Son, Le Hoang, Chiclana, Francisco, Kumar, Raghavendra, Mittal, Mamta, Khari, Manju, Chatterjee, Jyotir Moy, Baik, Sung Wook
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.08.2018; Elsevier Science Ltd
• Subjects: Algorithms; Animal migration optimization (AMO); Apriori algorithm; Association rules mining; Classification; Clustering; Computer memory; Computing time; Data mining; End users; Expert systems; Fitness; Migration; Optimization; Optimization algorithms; Particle swarm optimization (PSO); Animal migration optimization (AMO); Apriori algorithm; Particle swarm optimization (PSO); Association rules mining
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2018.04.038

Association rule mining (ARM) aims to find out association rules that satisfy predefined minimum support and confidence from a given database. However, in many cases ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results. In this paper, we propose a new mining algorithm based on animal migration optimization (AMO), called ARM–AMO, to reduce the number of association rules. It is based on the idea that rules which are not of high support and unnecessary are deleted from the data. Firstly, Apriori algorithm is applied to generate frequent itemsets and association rules. Then, AMO is used to reduce the number of association rules with a new fitness function that incorporates frequent rules. It is observed from the experiments that, in comparison with the other relevant techniques, ARM–AMO greatly reduces the computational time for frequent item set generation, memory for association rule generation, and the number of rules generated.