A New Algorithm for Frequent Itemsets Mining Based on Apriori and FP-Tree

Frequent item sets mining plays an important role in association rules mining. The apriori algorithm and the FP-growth algorithm are the most famous algorithms, existing frequent item sets mining algorithms are almost improved based on the two algorithms respectively and suffer from many problems when mining massive transctional datasets. In this paper, a new algorithm named APFT is proposed, it combines the Apriori algorithm and FP-tree structure which proposed in FP-growth algorithm. The advantage of APFT is that it dosen´t need to generate conditional pattern bases and sub- conditional pattern tree recursively. And the results of the experiments show that it works faster than Apriori and almost as fast as FP-growth.