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	Discovering maximum sequential patterns efficiently
	Lu, S ; Li, CP
	2010-05-07 ; 2010-05-07
会议名称	DMIN '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON DATA MINING ; International Conference on Data Mining (DMIN 05) ; Las Vegas, NV ; Web of Science
关键词	sequential patterns statistical model data mining Computer Science, Artificial Intelligence Computer Science, Information Systems
中文摘要	By analyzing the Apriori-like techniques [2, 3, 4] and the features of discovering frequent patterns on the large sets of transactions, we present an improved algorithm - AprioriAdjust based on a statistical model to decide the sequenced support, in which the convergence of average value of the support in the whole procedure is considered. Our aim is to,develop a feasible approach for pruning the candidate set of the frequent patterns efficiently and furthermore to improve the performance of discovering maximum sequential patterns.
会议录出版者	C S R E A PRESS ; ATHENS ; 115 AVALON DR, ATHENS, GA 30606 USA
语种	英语 ; 英语
内容类型	会议论文
源URL	[http://hdl.handle.net/123456789/16942]
专题	清华大学
推荐引用方式 GB/T 7714	Lu, S,Li, CP. Discovering maximum sequential patterns efficiently[C]. 见:DMIN '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON DATA MINING, International Conference on Data Mining (DMIN 05), Las Vegas, NV, Web of Science.

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