Efficient estimation of dynamic density functions with an application to outlier detection

Handle URI:
http://hdl.handle.net/10754/564486
Title:
Efficient estimation of dynamic density functions with an application to outlier detection
Authors:
Qahtan, Abdulhakim Ali Ali ( 0000-0001-8254-1764 ) ; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Wang, Suojin
Abstract:
In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Machine Intelligence & kNowledge Engineering Lab
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
Conference/Event name:
21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Issue Date:
2012
DOI:
10.1145/2396761.2398593
Type:
Conference Paper
ISBN:
9781450311564
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.authorZhang, Xiangliangen
dc.contributor.authorWang, Suojinen
dc.date.accessioned2015-08-04T07:02:13Zen
dc.date.available2015-08-04T07:02:13Zen
dc.date.issued2012en
dc.identifier.isbn9781450311564en
dc.identifier.doi10.1145/2396761.2398593en
dc.identifier.urihttp://hdl.handle.net/10754/564486en
dc.description.abstractIn this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.rights© ACM, 2012. 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 21st ACM international conference on Information and knowledge management - CIKM '12, http://dx.doi.org/10.1145/2396761.2398593en
dc.subjectdata streamsen
dc.subjectdensity estimationen
dc.subjectinterpolationen
dc.subjectoutlier detectionen
dc.titleEfficient estimation of dynamic density functions with an application to outlier detectionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Laben
dc.identifier.journalProceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12en
dc.conference.date29 October 2012 through 2 November 2012en
dc.conference.name21st ACM International Conference on Information and Knowledge Management, CIKM 2012en
dc.conference.locationMaui, HIen
dc.eprint.versionPost-printen
dc.contributor.institutionTexas A and M University, College Station, TX 77843-1372, United Statesen
kaust.authorZhang, Xiangliangen
kaust.authorQahtan, Abdulhakim Ali Alien
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