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	SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows
	Chang, Lei ; Wang, Tengjiao ; Yang, Dongqing ; Luan, Hua
	2008
英文摘要	Previous studies have shown mining closed patterns provides more benefits than mining the complete set of frequent patterns, since closed pattern mining leads to more compact results and more efficient algorithms. It is quite useful in a data stream environment where memory and computation power are major concerns. This paper studies the problem of mining closed sequential patterns over data stream sliding windows. A synopsis structure IST (Inverse Closed Sequence Tree) is designed to keep inverse closed sequential patterns in current window An efficient algorithm SeqStream is developed to mine closed sequential patterns in stream windows incrementally, and various novel strategies are adopted in SeqStream to prune search space aggressively. Extensive experiments on both real and synthetic data sets show that SeqStream outperforms PrefixSpan, CloSpan and BIDE by a factor of about one to two orders of magnitude.; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; EI; CPCI-S(ISTP); 4
语种	英语
DOI标识	10.1109/ICDM.2008.36
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/293507]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Chang, Lei,Wang, Tengjiao,Yang, Dongqing,et al. SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows. 2008-01-01.

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