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    KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

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    Type
    Article
    Authors
    Qahtan, Abdulhakim Ali Ali cc
    Wang, Suojin
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-11-08
    Online Publication Date
    2016-11-08
    Print Publication Date
    2017-03-01
    Permanent link to this record
    http://hdl.handle.net/10754/621861
    
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    Abstract
    Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.
    Citation
    Qahtan A, Wang S, Zhang X (2016) KDE-Track: An Efficient Dynamic Density Estimator for Data Streams. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2016.2626441.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2016.2626441
    Additional Links
    http://ieeexplore.ieee.org/document/7738463/
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2016.2626441
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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