Incremental Mining of Closed Sequential Patterns in Multiple Data Streams

doi:10.4304/jnw.6.5.728-735

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/78595

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题名:	Incremental Mining of Closed Sequential Patterns in Multiple Data Streams
作者:	Yang, Shih-yang;Chao, Ching-ming;Chen, Po-zung;Sun, Chu-hao
贡献者:	淡江大學資訊工程學系
关键词:	Multiple Data Streams;Data Stream Mining;Sequential Pattern Mining;Incremental Mining
日期:	2011-05
上传时间:	2012-10-17 10:53:34 (UTC+8)
出版者:	Oulu: Academy Publisher
摘要:	Sequential pattern mining searches for the relative sequence of events, allowing users to make predictions on discovered sequential patterns. Due to drastically advanced information technology over recent years, data have rapidly changed, growth in data amount has exploded and real-time demand is increasing, leading to the data stream environment. Data in this environment cannot be fully stored and ineptitude in traditional mining techniques has led to the emergence of data stream mining technology. Multiple data streams are a branch of the data stream environment. The MILE algorithm cannot preserve previously mined sequential patterns when new data are entered because of the concept of one-time fashion mining. To address this problem, we propose the ICspan algorithm to continue mining sequential patterns through an incremental approach and to acquire a more accurate mining result. In addition, due to the algorithm constraint in closed sequential patterns mining, the generation and records for sequential patterns will be reduced, leading to a decrease of memory usage and to an effective increase of execution efficiency.
關聯:	Journal of Networks 6(5), pp.728-735
DOI:	10.4304/jnw.6.5.728-735
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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