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

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    sp288-Qahtan.pdf
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    3.977Mb
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    Description:
    Conference paper manuscript
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    Type
    Conference Paper
    Authors
    Qahtan, Abdulhakim Ali Ali cc
    Zhang, Xiangliang cc
    Wang, Suojin
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2012
    Permanent link to this record
    http://hdl.handle.net/10754/564486
    
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    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.
    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
    ISBN
    9781450311564
    DOI
    10.1145/2396761.2398593
    ae974a485f413a2113503eed53cd6c53
    10.1145/2396761.2398593
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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