Optimization Algorithm of Association Rule Mining Based on Reducing the Time of Generating Candidate Itemset

Abstract

There are some problems about some optimization algorithms of Apriori such as they consume large memory space although they reduce the numbers of database scanning, or the problem about the difficulties to realize programming. This paper presents an Apriori's optimization algorithm. The algorithm first uses the order character of itemsets to reduce the times of comparison and connection when it connects and generates the candidate itemsets, then compresses the candidate itemsets according to the following situation: whether the number of element "a" in the frequent K-itemsets is less than K. Through the experiment, it is proved that the algorithm can not only realize programming easily but also improve the efficiency of mining association rules.