Minimizing Frequent Itemsets Using Hybrid ABCBAT Algorithm

The expansion in information technology field leads to the increase in amount of data collected. Huge amount of data is stored in databases, data warehouses and repositories. Data mining is the process of analyzing the database and extract the required information and finding the relationships among the items of datasets using association rule mining. Apriori is a familiar algorithm for association rule mining which generates frequent itemsets. In this paper, we propose a new algorithm called hybrid ABCBAT which minimizes the generation of frequent itemsets and also reduces the time, space and memory. In the proposed algorithm, ABC is hybridized with random walk of BAT algorithm. Random walk is used in the place of onlooker bee phase in order to increase the exploration. Hybrid ABCBAT algorithm is applied over the frequent itemsets gathered from apriori algorithm, to minimize frequent itemsets. Different datasets from UCI repository are considered for experiment. The proposed algorithm has better optimization accuracy, convergence rate and robustness.