• DocumentCode
    1575236
  • Title

    Introduction to spectral clustering

  • Author

    Hamad, Denis ; Biela, Philippe

  • Author_Institution
    LASL/ULCO, Calais
  • fYear
    2008
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Spectral clustering methods are based on graph and matrix theories. Their principle is simple: given some data inputs, build similarity matrix, analyse the spectrum of its Laplacian matrix, and often get a perfect clustering from the eigenvectors analysis. This paper presents an introduction to spectral clustering methods and some applications in signal and image segmentation.
  • Keywords
    eigenvalues and eigenfunctions; graph theory; matrix algebra; pattern clustering; Laplacian matrix; eigenvectors analysis; graph theory; matrix theory; similarity matrix; spectral clustering; Application software; Clustering algorithms; Clustering methods; Computer vision; Eigenvalues and eigenfunctions; Image segmentation; Laplace equations; Machine learning; Partitioning algorithms; Signal processing algorithms; Spectral methods; clustering; eigenvalue; graph; partition; segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on
  • Conference_Location
    Damascus
  • Print_ISBN
    978-1-4244-1751-3
  • Electronic_ISBN
    978-1-4244-1752-0
  • Type

    conf

  • DOI
    10.1109/ICTTA.2008.4529994
  • Filename
    4529994
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1575236