An Efficient Frequent Pattern Mining Algorithm

Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for other data mining tasks. FP-growth algorithm has been implemented using a prefix-tree structure, known as a FP-tree, for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But In FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel FP-array technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for sparse datasets. We then present a new algorithm which use the FP-tree data structure in combination with the FP-array technique efficiently and get the counts of frequent items from FP-array directly in order to omit the first scanning and save time. Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their memory consumption and their scalability.