EVALUATING THE PERFORMANCE OF ASSOCIATION RULE MINING ALGORITHMS

Abstract

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. In this paper, we present the performance comparison of Apriori and FP-growth algorithms. The performance is analyzed based on the execution time for different number of instances and confidence in Super market data set. These algorithms are presented together with some experimental data. Our performance study shows that the FP-growth method is efficient and scalable and is about an order of magnitude faster than the Apriori algorithm.