点击次数：

论文类型：期刊论文

发表时间：2013-01-01

发表刊物：Journal of Computers (Finland)

收录刊物：EI、Scopus

卷号：8

期号：6

页面范围：1417-1426

ISSN号：1796203X

摘要：Frequent itemset/pattern mining (FIM) over uncertain transaction dataset is a fundamental task in data mining. In this paper, we study the problem of FIM over uncertain datasets. There are two main approaches for FIM: the level-wise approach and the pattern-growth approach. The level-wise approach requires multiple scans of dataset and generates candidate itemsets. The pattern-growth approach requires a large amount of memory and computation time to process tree nodes because the current algorithms for uncertain datasets cannot create a tree as compact as the original FP-Tree. In this paper, we propose an array based tail node tree structure (namely AT-Tree) to maintain transaction itemsets, and a pattern-growth based algorithm named AT-Mine for FIM over uncertain dataset. AT-Tree is created by two scans of dataset and it is as compact as the original FP-Tree. AT-Mine mines frequent itemsets from AT-Tree without additional scan of dataset. We evaluate our algorithm using sparse and dense datasets; the experimental results show that our algorithm has achieved better performance than the state-of-the-art FIM algorithms on uncertain transaction datasets, especially for small minimum expected support number. ? 2013 ACADEMY PUBLISHER.