• DocumentCode
    3167032
  • Title

    Using a semisupervised fuzzy clustering process for identity identification in digital libraries

  • Author

    Diaz-Valenzuela, Irene ; Martin-Bautista, Maria J. ; Amparo Vila, M.

  • Author_Institution
    Dept. of Comput. Sci. & Artificial Intell., Univ. of Granada, Granada, Spain
  • fYear
    2013
  • fDate
    24-28 June 2013
  • Firstpage
    831
  • Lastpage
    836
  • Abstract
    This paper introduces a new semisupervised fuzzy algorithm that makes use of must-link and cannot-link constraints. These constraints are applied to the process of finding the optimum α-cut of a dendrogram. We have applied this method to identity identification in digital libraries.
  • Keywords
    digital libraries; fuzzy set theory; learning (artificial intelligence); pattern clustering; trees (mathematics); cannot-link constraints; digital libraries; identity identification; must-link constraints; optimum dendogram α-cut; semisupervised fuzzy clustering process; Analysis of variance; Clustering algorithms; Computer science; Electronic mail; Libraries; Manganese; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint
  • Conference_Location
    Edmonton, AB
  • Type

    conf

  • DOI
    10.1109/IFSA-NAFIPS.2013.6608508
  • Filename
    6608508
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3167032