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dc.contributor.authorQahtan, Abdulhakim Ali Ali
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorWang, Suojin
dc.date.accessioned2015-08-04T07:02:13Z
dc.date.available2015-08-04T07:02:13Z
dc.date.issued2012
dc.identifier.isbn9781450311564
dc.identifier.doi10.1145/2396761.2398593
dc.identifier.urihttp://hdl.handle.net/10754/564486
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.
dc.publisherAssociation for Computing Machinery (ACM)
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/10.1145/2396761.2398593
dc.subjectdata streams
dc.subjectdensity estimation
dc.subjectinterpolation
dc.subjectoutlier detection
dc.titleEfficient estimation of dynamic density functions with an application to outlier detection
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journalProceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
dc.conference.date29 October 2012 through 2 November 2012
dc.conference.name21st ACM International Conference on Information and Knowledge Management, CIKM 2012
dc.conference.locationMaui, HI
dc.eprint.versionPost-print
dc.contributor.institutionTexas A and M University, College Station, TX 77843-1372, United States
kaust.personZhang, Xiangliang
kaust.personQahtan, Abdulhakim Ali Ali
refterms.dateFOA2018-06-14T07:56:40Z


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