標題: Mining Temporal Patterns in Time Interval-Based Data
作者: Chen, Yi-Cheng
Peng, Wen-Chih
Lee, Suh-Yin
資訊工程學系
Department of Computer Science
關鍵字: Data mining;representation;sequential pattern;temporal pattern;interval-based event
公開日期: 1-十二月-2015
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.
URI: http://dx.doi.org/10.1109/TKDE.2015.2454515
http://hdl.handle.net/11536/129365
ISSN: 1041-4347
DOI: 10.1109/TKDE.2015.2454515
期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume: 27
Issue: 12
起始頁: 3318
結束頁: 3331
顯示於類別:期刊論文