• DocumentCode
    1013213
  • Title

    Performance of kernel-based fuzzy clustering

  • Author

    Graves, D. ; Pedrycz, W.

  • Author_Institution
    Univ. of Alberta, Edmonton
  • Volume
    43
  • Issue
    25
  • fYear
    2007
  • Firstpage
    1445
  • Lastpage
    1446
  • Abstract
    An evaluation and comparative study of kernel-based fuzzy clustering algorithms is presented. The main objective is to evaluate the performance gains provided by kernelised FCM (fuzzy C-means). It is shown that kernelised FCM provides marginal improvements in the classification rate for several popular Machine Learning data sets. It is observed that the performance of kernelised FCM depends greatly on the selection of the kernel parameters.
  • Keywords
    fuzzy set theory; learning (artificial intelligence); pattern clustering; fuzzy C-means; kernel-based fuzzy clustering; kernelised FCM; machine learning;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:20073093
  • Filename
    4405613
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1013213