• DocumentCode
    710050
  • Title

    Interval type-2 fuzzy c-means clustering using intuitionistic fuzzy sets

  • Author

    Dzung Dinh Nguyen ; Long Thanh Ngo ; Long The Pham

  • Author_Institution
    Dept. of Inf. Syst., Le Quy Don Tech. Univ., Hanoi, Vietnam
  • fYear
    2013
  • fDate
    15-18 Dec. 2013
  • Firstpage
    299
  • Lastpage
    304
  • Abstract
    In this paper, intuitionistic interval type-2 fuzzy c-means clustering (InIT2FCM) method is proposed for the clustering problems. Intuitionistic fuzzy sets (IFS) and intuitionistic type-2 fuzzy sets (InIT2FS) were introduced with the aim to better handle the uncertainty. Utilizing the advantages of the IFS and InT2FS, we have combined them with fuzzy clustering algorithms to overcome some drawbacks of the “conventional” FCM in handling uncertainty. The experiments were completed for different types of images which show the advantages of the proposed algorithms, especially with noisy images.
  • Keywords
    fuzzy set theory; image processing; pattern clustering; IFS; InIT2FCM method; InIT2FS; InT2FS; intuitionistic interval type-2 fuzzy c-means clustering algorithm; intuitionistic type-2 fuzzy sets; noisy images; uncertainty handling; Integrated circuits; Uncertainty; Intuitionistic fuzzy c-means clustering; Intuitionistic fuzzy sets; intuitionistic type-2 fuzzy sets; type-2 fuzzy c-means clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Communication Technologies (WICT), 2013 Third World Congress on
  • Conference_Location
    Hanoi
  • Type

    conf

  • DOI
    10.1109/WICT.2013.7113152
  • Filename
    7113152