• DocumentCode
    2975416
  • Title

    Interval ckMeans: An algorithm for clustering symbolic data

  • Author

    De Vargas, Rogério R. ; Bedregal, Benjamín R C

  • Author_Institution
    Dept. of Inf. & Appl. Math., Fed. Univ. of Rio Grande do Norte, Natal, Brazil
  • fYear
    2011
  • fDate
    18-20 March 2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Clustering is the process of organizing a collection of patterns into groups based on their similarities. Fuzzy clustering techniques aim at finding groups to which every object in the database belongs to some membership degree. This paper presents a new algorithm for clustering symbolic data based on ckMeans algorithm. This new algorithm allows the data entry and the membership degree to be intervals. In order to validate the proposal, it is compared to two other algorithms using the same database.
  • Keywords
    data analysis; fuzzy set theory; pattern clustering; fuzzy clustering technique; interval ckMeans algorithm; membership degree; pattern collection organization; symbolic data clustering; Cities and towns; Clustering algorithms; Equations; Measurement; Partitioning algorithms; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society (NAFIPS), 2011 Annual Meeting of the North American
  • Conference_Location
    El Paso, TX
  • ISSN
    Pending
  • Print_ISBN
    978-1-61284-968-3
  • Electronic_ISBN
    Pending
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2011.5752042
  • Filename
    5752042
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2975416