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论文类型：期刊论文

发表时间：2014-01-01

发表刊物：Journal of Software

收录刊物：EI、Scopus

卷号：9

期号：1

页面范围：44-56

ISSN号：1796217X

摘要：In this paper, we study the problem of finding frequent itemsets from uncertain data streams. To the best of our knowledge, the existing algorithms cannot compress transaction itemsets to a tree as compact as the classical FPTree, thus they need much time and memory space to process the tree. To address this issue, we propose an algorithm UDS-FIM and a tree structure UDS-Tree. Firstly, UDS-FIM maintains probability values of each transactions to an array; secondly, compresses each transaction to a UDS-Tree in the same manner as an FP-Tree (so it is as compact as an FP-Tree) and maintains index of probability values of each transaction in the array to the corresponding tail-nodes; lastly, it mines frequent itemsets from the UDSTree without additional scan of transactions. The experimental results show that UDS-FIM has achieved a good performance under different experimental conditions in terms of runtime and memory consumption. ? 2014 ACADEMY PUBLISHER.