• DocumentCode
    2037066
  • Title

    A kernel-based fuzzy c-means algorithm with partition index maximization

  • Author

    Tsai, Hsu-Shen ; Yang, Miin-Shen

  • Author_Institution
    Dept. of Manage. Inf. Syst., Takming Univ. of Sci. & Technol., Taipei, Taiwan
  • Volume
    1
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    391
  • Lastpage
    394
  • Abstract
    This paper presents a kernel-based fuzzy c-means algorithm with partition index maximization, called KPIM algorithm. The proposed KPIM algorithm is more robust than the partition index maximization algorithm proposed by Özdemir and Akarum. Experiments show that the advantage of KPIM are robust properties: (1) robust to fuzziness parameter m, (2) robust to outlier, (3) robust to image artifacts; and fast computational performance. Especially, KPIM can overcome drawbacks of PIM, and are well used in image segmentation.
  • Keywords
    fuzzy set theory; image segmentation; optimisation; pattern clustering; KPIM algorithm; image artifacts; image segmentation; kernel-based fuzzy c-means algorithm; partition index maximization algorithm; Clustering algorithms; Gaussian noise; Image segmentation; Indexes; Kernel; Partitioning algorithms; Robustness; Fuzzy c-means(FCM); Image segmentation; Kernel; Outlier; Partition index maximization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5931-5
  • Type

    conf

  • DOI
    10.1109/FSKD.2010.5569636
  • Filename
    5569636
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2037066