Effective algorithm of mining frequent itemsets for association rules

Abstract

The efficiency of mining association rules is an important field of KDD. The algorithm Apriori is a classical algorithm in mining association rules. It is a breadth first search on the lattice space of itemsets. Though it makes use of anti-monotone of itemsets to reduce searching breadth, the algorithmic complexity of time is still the exponential quantity. In this article, the concepts of the generation and the ordinal itemsets tree are introduced. The ordinal itemsets tree is the dynamic description of mining relation of itemsets, and the vegetal ability of the ordinal itemsets tree is described by the generation. Through the study of the association rules, the conclusion that all frequent itemsets are not all vegetal itemsets and all vegetal itemsets are all frequent itemsets is discovered. With this conclusion, the number of the candidate itemsets can be reduced further to improve the efficiency of mining association rules and reduce the searching breadth. According to the generation, the AprioriFREQ algorithm, which is the improvement algorithm of Apriori, is designed in this article. By testing, the efficiency of the AprioriFREQ algorithm is obviously higher than the Apriori's.