• DocumentCode
    598682
  • Title

    Principal points estimation using mixture distributions

  • Author

    Ueki, D. ; Matsuura, Saeko ; Suzuki, Hajime

  • Author_Institution
    Grad. Sch. of Sci. & Technol., Keio Univ., Yokohama, Japan
  • fYear
    2012
  • fDate
    1-2 Dec. 2012
  • Firstpage
    219
  • Lastpage
    222
  • Abstract
    The k-principal points of a distribution are the k points that optimally partition the distribution. In this paper, we propose a method to estimate principal points from data by using mixture distributions when we have no prior knowledge of the distribution of data. Several simulation results are presented to compare the proposed method with the nonparametric k-means.
  • Keywords
    data analysis; estimation theory; statistical distributions; data distributions; k-principal points; mixture distributions; nonparametric k-means method; principal points estimation; Clustering algorithms; Data models; Educational institutions; Estimation; Gaussian distribution; Partitioning algorithms; Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Science and Information Systems (ICACSIS), 2012 International Conference on
  • Conference_Location
    Depok
  • Print_ISBN
    978-1-4673-3026-8
  • Type

    conf

  • Filename
    6468726
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=598682