• DocumentCode
    3709044
  • Title

    A comparison study of clustering validity indices

  • Author

    Hasna Chouikhi;Malika Charrad;Nadia Ghazzali

  • Author_Institution
    University of Gabes, Tunisia
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Most of the algorithms of clustering take in entry certain parameters such as the number and the density of clusters or, at least, the number of data in every cluster. The question that arises is how to determine the number of clusters in the case of an automatic classification. The purpose of our study is to compare cluster validity indices to select the optimal ones. An examination of 30 indices for determining the number of clusters is conducted on real and artificial data sets being generated according to various design factors.
  • Keywords
    "Indexes","Clustering algorithms","Biometrics (access control)","Data mining","Partitioning algorithms","Clustering methods","Euclidean distance"
  • Publisher
    ieee
  • Conference_Titel
    Computer & Information Technology (GSCIT), 2015 Global Summit on
  • Print_ISBN
    978-1-4673-6586-4
  • Type

    conf

  • DOI
    10.1109/GSCIT.2015.7353330
  • Filename
    7353330
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3709044