Efficiently Using Prefix-trees in Mining Frequent Itemsets.
Gösta Grahne, Jianfei Zhu · 2003
Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent itemsets. Numerous experimental results have demonstrated that these algorithms perform extremely well. In this paper we present a novel array-based technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. Our technique works especially well for sparse datasets. Furthermore,