Efficiently Using Prefix-trees in Mining Frequent Itemsets.

A novel array-based technique is presented that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FPtree based algorithms, and works especially well for sparse datasets.

Abstract

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,