Incrementally Mining Temporal Patterns in Interval-based Databases

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題名:	Incrementally Mining Temporal Patterns in Interval-based Databases
作者:	Chen, Yi-Cheng;Weng, Julia Tzu-Ya;Wang, Jun-Zhe;Chou, Chien-Li;Huang, Jiun-Long;Lee, Suh-Yin
貢獻者:	資訊工程學系暨研究所
關鍵詞:	dynamic representation;incremental mining;interval-based pattern;sequential pattern mining
日期:	2014-11-01
上傳時間:	2014-10-30 17:51:02 (UTC+8)
出版者:	IEEE
摘要:	In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.
關聯:	The 2014 International Conference on Data Science and Advanced Analytics
顯示於類別:	[資訊工程學系暨研究所] 會議論文

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