Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints

doi:10.6186/IJIMS.2016.27.2.4

淡江大學機構典藏 > 淡江出版期刊 > 資訊與管理科學期刊 > 第27卷第2期 > Item 987654321/109383

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题名:	Discovering Time-Interval Sequential Patterns by a Pattern Growth Approach with Confidence Constraints
作者:	Shyur, Huan-Jyh;Jou, Chichang;Cheng, Chi-Bin;Yen, Chih-Yu
关键词:	Sequential pattern mining;time-interval sequential patterns;pattern growth;confidence
日期:	2016
上传时间:	2017-01-17 11:27:44 (UTC+8)
出版者:	淡江大學出版中心
摘要:	Sequential pattern mining is to discover frequent sequential patterns in a sequence database. The technique is applied to fields such as web click-stream mining, failure forecast, and traf- fic analysis. Conventional sequential pattern-mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a valuable information when the time of the occurrence of an event is concerned. This study extends the concept of the well-known pattern growth approach, PrefixSpan algorithm, to propose a novel sequential pattern mining approach for sequential patterns with time intervals. Unlike the other time-interval sequential pattern-mining algorithms, the approach concerns the time for the next event to occur more than the timing information with its precedent events. To obtain a more reliable sequential pattern, a new measure of the confidence of a sequential pattern is defined. Experiments are conducted to evaluate the performance of the proposed approach.
關聯:	nternational Journal of Information and Management Sciences 27 (2), pp.129-145
DOI:	10.6186/IJIMS.2016.27.2.4
显示于类别:	[資訊與管理科學期刊] 第27卷第2期

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