• DocumentCode
    2457643
  • Title

    The Approach of Adaptive Spectral Clustering Analyze on High Dimensional Data

  • Author

    Cai, Liping ; Zhou, Xuchuan ; Song, Jancheng

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Southwest Univ. for Nat., Chengdu, China
  • fYear
    2010
  • fDate
    17-19 Dec. 2010
  • Firstpage
    160
  • Lastpage
    162
  • Abstract
    Data mining is an important tool in knowledge discovery. It aims at discovering the valuable patterns hidden in large volume of data. In a distributed environment, the main problem we need to consider for data mining is how to transfer the minimal amount of data and provide maximum sharing of information with the continued expansion of business scale and constant update of services content. For high-dimensional scientific data, an adaptive spectral clustering method has been proposed. The experimental results on numerical simulation of scientific data have shown the improvement of proposed method.
  • Keywords
    data mining; pattern clustering; adaptive spectral clustering; data mining; high dimensional scientific data; information sharing; knowledge discovery; numerical simulation; Algorithm design and analysis; Clustering algorithms; Data mining; Data models; Nearest neighbor searches; Principal component analysis; Simulation; High Dimensional Data; Projection; Spectral Clustering; Subspace;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Information Sciences (ICCIS), 2010 International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-8814-8
  • Electronic_ISBN
    978-0-7695-4270-6
  • Type

    conf

  • DOI
    10.1109/ICCIS.2010.45
  • Filename
    5709038