Analysis on Efficiency of Apriori and MBAT Algorithms

Abstract

Data Mining is a fast developing field of computer science and technology, which are helpful to enable end users for decision making process. One of the most important data mining processes is that of Association Rule Mining. This paper intends to the analysis on efficiency of the two algorithms (Apriori and MBAT) which finding frequent itemsets in Association Rule Mining. The Association Rule Mining is based mainly on discovering frequent itemsets. Apriori algorithm and other popular Association Rule Mining algorithms mainly generate a large number of candidate items and scanning the database too many times. To remove these deficiencies, this paper presents a method named Matrix Based Frequent Itemsets Minining algorithm with Tags (MBAT) which can reduce the number of candidate itemsets. In this paper, the system used Java Programming Language with Follow Me products dataset to compare these two algorithms.