Mining temporal patterns in interval-based data

Yi Cheng Chen, Wen-Chih Peng, Suh Yin Lee

研究成果: Conference contribution同行評審

19 引文 斯高帕斯(Scopus)

摘要

Sequential pattern mining is an important subfield in data mining. Recently, discovering patterns from interval events has attracted considerable efforts due to its widespread applications. However, due to the complex relation between two intervals, mining interval-based sequences efficiently is a challenging issue. In this paper, we develop a novel algorithm, P-TPMiner, to efficiently discover two types of interval-based sequential patterns. Some pruning techniques are proposed to further reduce the search space of the mining process. Experimental studies show that proposed algorithm is efficient and scalable. Furthermore, we apply proposed method to real datasets to demonstrate the practicability of discussed patterns.

原文English
主出版物標題2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1506-1507
頁數2
ISBN(電子)9781509020195
DOIs
出版狀態Published - 22 6月 2016
事件32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
持續時間: 16 5月 201620 5月 2016

出版系列

名字2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Conference

Conference32nd IEEE International Conference on Data Engineering, ICDE 2016
國家/地區Finland
城市Helsinki
期間16/05/1620/05/16

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