• DocumentCode
    456524
  • Title

    Noise Clustering using a New Distance

  • Author

    Wu, Xiao-Hong ; Zhou, Jian-Jiang

  • Author_Institution
    Coll. of Inf. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut.
  • Volume
    1
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    1938
  • Lastpage
    1943
  • Abstract
    Based on a new distance, a novel noise-resistant fuzzy clustering algorithm called alternative noise clustering (ANC) algorithm is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave (1993). By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical point of view and influence function, the non-Euclidean distance is more robust than the Euclidean distance. So the ANC algorithm is more robust than the NC algorithm. Moreover, with the new distance ANC can deal with noises or outliers better than NC and fuzzy c-means (FCM). The better performance of the proposed algorithm is shown by performing experiments on data sets
  • Keywords
    fuzzy set theory; noise; pattern clustering; alternative noise clustering; noise-resistant fuzzy clustering; nonEuclidean distance; Clustering algorithms; Computer vision; Digital images; Educational institutions; Equations; Euclidean distance; Noise robustness; Partitioning algorithms; Pattern recognition; Space technology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Communication Technologies, 2006. ICTTA '06. 2nd
  • Conference_Location
    Damascus
  • Print_ISBN
    0-7803-9521-2
  • Type

    conf

  • DOI
    10.1109/ICTTA.2006.1684686
  • Filename
    1684686