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论文类型：期刊论文

发表时间：2013-12-01

发表刊物：Journal of Computational Information Systems

收录刊物：EI、Scopus

卷号：9

期号：23

页面范围：9261-9268

ISSN号：15539105

摘要：Probabilistic frequent itemset mining plays a significant role in association rule mining over uncertain data, which means to obtain itemsets with existential probability larger than a minimum threshold. So far, several studies have focused on this kind of work, however, two problems brought to users are: how to select an appropriate threshold for the mining and how to reflect their concern to different items. Against these issues, in this paper, we provide a framework for mining k weight-based probabilistic itemsets that user expects most. The concept of item weight is newly added to uncertain data mining, and k refers to the number of itemsets that user desires. Then algorithm tkWUG is proposed as an extension of the traditional UF-Growth. Moreover, a strategy is provided to solve the challenges brought by the new framework. Results on real and synthetic datasets show and demonstrate the accurancy and efficiency of new algorithm. Copyright ? 2013 Binary Information Press.