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    Data Stream Clustering With Affinity Propagation

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    TKDE.pdf
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    1.108Mb
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Zhang, Xiangliang cc
    Furtlehner, Cyril
    Germain-Renaud, Cecile
    Sebag, Michele
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2013-08-23
    Online Publication Date
    2013-08-23
    Print Publication Date
    2014-07
    Permanent link to this record
    http://hdl.handle.net/10754/556655
    
    Metadata
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    Abstract
    Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.
    Citation
    Data Stream Clustering With Affinity Propagation 2014, 26 (7):1644 IEEE Transactions on Knowledge and Data Engineering
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2013.146
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6585253
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2013.146
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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