The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkAssociation 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
