An Improved Association Rule Mining Algorithm Based on Apriori and Ant Colony approaches

Abstract

The Knowledge Discovery in Databases (KDD) field of data mining is useful in finding trends, patterns and anomalies in the databases which is helpful to make accurate decisions for the future. Association rule mining is an important topic in data mining field. Association rule mining finds collections of data attributes that are statistically related to the data available. Apriori algorithm generates all significant association rules between items in the database. Besides, ACO algorithms are probabilistic techniques for solving computational problems that are based in finding as good as possible paths through graphs by imitating the ants' search for food. The use of such techniques has been very successful for several problems. The collaborative use of ACO and DM (the use of ACO algorithms for DM tasks) is a very promising direction. In this paper, based on association rule mining and Apriori algorithm, an improved Ant Colony algorithm is proposed to solve the Frequent Pattern Mining problem. Ant colony algorithm is employed as evolutionary algorithm to optimize the obtained set of association rules produced using Apriori algorithm. The results and comparison of the method is shown at the end of the paper.