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 FPtree data structure in combination with the FP-array technique efficiently.In the algorithm,an efficient closenesstesting 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.
Closed FP-growth algorithm Closed FP-tree sparse datasets FP-array
Jun TAN Yingyong BU Bo YANG
College of Mechanical and Electrical Engineering Central South University Changsha, Hunan Province, China
国际会议
长沙
英文
528-531
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）