Optimize Association Rules Using Artificial Bee Colony Algorithm with Mutation

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using 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, artificial bee colony algorithm is used to generate high quality association rules for finding frequent item sets from large data sets. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using artificial bee colony algorithm (ABC) over these generated rules, the system can predict the rules which contains negative attributes. Proposed methodology is compared with K-nearest neighbors (KNN) algorithm and standard ABC algorithms.