A New Improvement on Apriori Algorithm

Efficiency has been concerned for several years in the research of association rules mining. In this paper, based on the improvement on the classical Apriori algorithm, a high-dimension oriented Apriori algorithm is proposed. Unlike existed Apriori improvements, our algorithm adopts a new method to reduce the redundant generation of sub-itemsets during pruning the candidate itemsets, which can obtain higher efficiency of mining than that of the original algorithm when the dimension of data is high. Theoretical proof and analysis are given for the rationality of our algorithm. Experiments with datasets of KDD Cup 1999 validate our work