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Discovering multi-label temporal patterns in sequence databases

Authors:

Yen-Liang Chen,

Yu-Cheng WangAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 181, Issue 3

Pages 398 - 418

https://doi.org/10.1016/j.ins.2010.09.024

Published: 01 February 2011 Publication History

Abstract

Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.

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Cited By

Fan YShi YKang KXing Q(2019)An Inflection Point Based Clustering Method for Sequence DataWeb Information Systems and Applications10.1007/978-3-030-30952-7_22(201-212)Online publication date: 20-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30952-7_22
Huang CYang PHsieh K(2018)Knowledge discovery of consensus and conflict interval-based temporal patternsKnowledge-Based Systems10.1016/j.knosys.2017.11.002140:C(201-213)Online publication date: 15-Jan-2018
https://dl.acm.org/doi/10.1016/j.knosys.2017.11.002
Gardezi SFaye ISanchez Bornot JKamel NHussain M(2018)Mammogram classification using dynamic time warpingMultimedia Tools and Applications10.1007/s11042-016-4328-877:3(3941-3962)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-016-4328-8
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 181, Issue 3

February, 2011

320 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2010.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 February 2011

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Cited By

Fan YShi YKang KXing Q(2019)An Inflection Point Based Clustering Method for Sequence DataWeb Information Systems and Applications10.1007/978-3-030-30952-7_22(201-212)Online publication date: 20-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30952-7_22
Huang CYang PHsieh K(2018)Knowledge discovery of consensus and conflict interval-based temporal patternsKnowledge-Based Systems10.1016/j.knosys.2017.11.002140:C(201-213)Online publication date: 15-Jan-2018
https://dl.acm.org/doi/10.1016/j.knosys.2017.11.002
Gardezi SFaye ISanchez Bornot JKamel NHussain M(2018)Mammogram classification using dynamic time warpingMultimedia Tools and Applications10.1007/s11042-016-4328-877:3(3941-3962)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-016-4328-8
Ma HJia MZhang DLin X(2017)Combining tag correlation and user social relation for microblog recommendationInformation Sciences: an International Journal10.1016/j.ins.2016.12.047385:C(325-337)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.12.047
Achar ASastry P(2015)Statistical significance of episodes with general partial ordersInformation Sciences: an International Journal10.1016/j.ins.2014.09.063296:C(175-200)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.09.063
Hamrouni TBen Yahia SMephu Nguifo E(2013)Looking for a structural characterization of the sparseness measure of (frequent closed) itemset contextsInformation Sciences: an International Journal10.1016/j.ins.2012.08.005222(343-361)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.08.005
Li PLi HWu M(2013)Multi-label ensemble based on variable pairwise constraint projectionInformation Sciences: an International Journal10.1016/j.ins.2012.07.066222(269-281)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.07.066
ÁLvarez MFéLix PCariñEna P(2013)Discovering metric temporal constraint networks on temporal databasesArtificial Intelligence in Medicine10.1016/j.artmed.2013.03.00658:3(139-154)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.artmed.2013.03.006
Yu DYu XHu QLiu JWu A(2011)Dynamic time warping constraint learning for large margin nearest neighbor classificationInformation Sciences: an International Journal10.1016/j.ins.2011.03.001181:13(2787-2796)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.ins.2011.03.001

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