Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Top Down FP-Growth for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Hi-index | 0.00 |
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 wen 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.