• DocumentCode
    3261019
  • Title

    Unsupervised Clustering In Streaming Data

  • Author

    Tasoulis, Dimitris K. ; Adams, Niall M. ; Hand, David J.

  • Author_Institution
    Inst. for Math. Sci., Imperial Coll., London
  • fYear
    2006
  • fDate
    Dec. 2006
  • Firstpage
    638
  • Lastpage
    642
  • Abstract
    Tools for automatically clustering streaming data are becoming increasingly important as data acquisition technology continues to advance. In this paper we present an extension of conventional kernel density clustering to a spatio-temporal setting, and also develop a novel algorithmic scheme for clustering data streams. Experimental results demonstrate both the high efficiency and other benefits of this new approach
  • Keywords
    data mining; pattern clustering; conventional kernel density clustering; data clustering; streaming data; unsupervised clustering; Clustering algorithms; Clustering methods; Data acquisition; Data mining; Data models; Databases; Educational institutions; Kernel; Partitioning algorithms; Scalability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    0-7695-2702-7
  • Type

    conf

  • DOI
    10.1109/ICDMW.2006.165
  • Filename
    4063703
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3261019