A PCA-Based Change Detection Framework for Multidimensional Data Streams

Handle URI:
http://hdl.handle.net/10754/567035
Title:
A PCA-Based Change Detection Framework for Multidimensional Data Streams
Authors:
Qahtan, Abdulhakim Ali Ali ( 0000-0001-8254-1764 ) ; Alharbi, Basma Mohammed ( 0000-0001-5399-2320 ) ; Wang, Suojin; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.
KAUST Department:
Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
Conference/Event name:
21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
Issue Date:
10-Aug-2015
DOI:
10.1145/2783258.2783359
Type:
Conference Paper
Additional Links:
http://dl.acm.org/citation.cfm?doid=2783258.2783359
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorQahtan, Abdulhakim Ali Alien
dc.contributor.authorAlharbi, Basma Mohammeden
dc.contributor.authorWang, Suojinen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2015-08-16T12:46:25Zen
dc.date.available2015-08-16T12:46:25Zen
dc.date.issued2015-08-10en
dc.identifier.doi10.1145/2783258.2783359en
dc.identifier.urihttp://hdl.handle.net/10754/567035en
dc.description.abstractDetecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.en
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)en
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=2783258.2783359en
dc.rights© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 10-13, 2015.http://doi.acm.org/10.1145/2783258.2783359en
dc.titleA PCA-Based Change Detection Framework for Multidimensional Data Streamsen
dc.typeConference Paperen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15en
dc.conference.date2015-08-10 to 2015-08-13en
dc.conference.name21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015en
dc.conference.locationSydney, NSW, AUSen
dc.eprint.versionPost-printen
dc.contributor.institutionTexas A&M University, College Station, TX, USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorZhang, Xiangliangen
kaust.authorQahtan, Abdulhakim Ali Alien
kaust.authorAlharbi, Basma Mohammeden
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