@inproceedings{31920325901f4b6db87523e6762baf12,
title = "Mining temporal patterns in interval-based data",
abstract = "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.",
keywords = "data mining, interval-based event, representation, sequential pattern, temporal pattern",
author = "Chen, {Yi Cheng} and Peng, {Wen Chih} and Lee, {Suh Yin}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 32nd IEEE International Conference on Data Engineering, ICDE 2016 ; Conference date: 16-05-2016 Through 20-05-2016",
year = "2016",
month = jun,
day = "22",
doi = "10.1109/ICDE.2016.7498397",
language = "???core.languages.en_GB???",
series = "2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1506--1507",
booktitle = "2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016",
}