An Efficient Close 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 perform 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. We then present a very effective closed frequent pattern algorithm which uses a variation of the FP-tree data structure in combination with the FP-array technique efficiently. In the algorithm, an efficient closeness-testing approach is also given for mining closed frequent itemsets. Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their scalability.