• DocumentCode
    3262161
  • Title

    MovStream: An efficient algorithm for monitoring clusters evolving in data streams

  • Author

    Tang, Liang ; Tang, Chang-jie ; Duan, Lei ; Li, Chuan ; Jiang, Ye-xi ; Zeng, Chun-qiu ; Zhu, Jun

  • Author_Institution
    Sch. of Comput. Sci., Sichuan Univ., Chengdu
  • fYear
    2008
  • fDate
    26-28 Aug. 2008
  • Firstpage
    582
  • Lastpage
    587
  • Abstract
    Monitoring cluster evolution in data streams is a major research topic in data streams mining. Previous clustering methods for evolving data streams focus on global clustering result. It may lose critical information about individual cluster. This paper introduces some basic movements of evolution of an individual cluster. Based on the measurement of the movements, a novel algorithm called MovStream is proposed to monitor clusterspsila evolving in data streams. The experimental results on real datasets show that our MovStream algorithm surpasses the well-known CluStream algorithm by 25-50% in accuracy and one order of magnitude in efficiency.
  • Keywords
    data mining; pattern clustering; cluster evolution monitoring; data stream mining; movstream algorithm; Biomedical monitoring; Birth disorders; Clustering algorithms; Clustering methods; Computer science; Computerized monitoring; Data mining; Event detection; Gaussian distribution; Motion measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing, 2008. GrC 2008. IEEE International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4244-2512-9
  • Electronic_ISBN
    978-1-4244-2513-6
  • Type

    conf

  • DOI
    10.1109/GRC.2008.4664715
  • Filename
    4664715