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Association Rules Optimization using ABC Algorithm with Mutation
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In data mining, association rule mining is one of the popular and simple methods to find frequent itemsets from a large dataset. While generating frequent itemsets from a large dataset using association rule mining, the computer takes too much time. This can be improved by using an artificial bee colony algorithm (ABC). The artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, an artificial bee colony algorithm with a mutation operator is used to generate high-quality association rules for finding frequent itemsets from large data sets. The mutation operator is used after the scout bee phase in this work. In general, the rule generated by the association rule mining technique does not consider the negative occurrences of attributes in them, but by using an artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contain negative attributes.
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