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

淡江大學機構典藏 > 商管學院 > 資訊管理學系暨研究所 > 會議論文 > Item 987654321/108703

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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, Chi-Chang;Cheng, Chi-Bin;Yen, Chih-Yu
关键词:	Sequential pattern mining;Time-interval sequential patterns;Pattern growth;Confidence
日期:	2016-05-14
上传时间:	2016-12-03 02:11:28 (UTC+8)
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 traffic analysis. Conventional sequential pattern mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a more 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. The current study suggests that the confidence of the occurrence of a pattern is also important other than the frequency (i.e. support) of the pattern. Thus, the proposed approach extracts a pattern by first satisfying a minimum confidence constraint, and then finds out the least time interval that satisfies the minimum support constraint. Experiments are conducted to evaluate the performance of the proposed approach.
關聯:	論文集
显示于类别:	[資訊管理學系暨研究所] 會議論文

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