Mining Temporal Patterns in Time Interval-Based Data

doi:10.1109/TKDE.2015.2454515

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 987654321/106215

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題名:	Mining Temporal Patterns in Time Interval-Based Data
作者:	Chen, Yi-Cheng;Peng, Wen-Chih;Lee, Suh-Yin
關鍵詞:	data mining;interval-based event;representation;sequential pattern;temporal pattern
日期:	2015-12
上傳時間:	2016-04-22 13:42:04 (UTC+8)
出版者:	IEEE
摘要:	Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.
關聯:	IEEE Transactions on Knowledge and Data Engineering 27(12), p.3318-3331
DOI:	10.1109/TKDE.2015.2454515
顯示於類別:	[資訊工程學系暨研究所] 期刊論文

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