• DocumentCode
    3458573
  • Title

    Noise Clustering Using a New Distance

  • Author

    Wu, Xiao-Hong

  • Author_Institution
    Coll. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang
  • fYear
    2006
  • fDate
    20-23 Aug. 2006
  • Firstpage
    1015
  • Lastpage
    1020
  • 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. 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; pattern clustering; statistical analysis; alternative noise clustering algorithm; fuzzy c-means method; noise-resistant fuzzy clustering algorithm; nonEuclidean distance; statistical analysis; Clustering algorithms; Computer vision; Digital images; Educational institutions; Euclidean distance; Fuzzy sets; Noise robustness; Partitioning algorithms; Pattern recognition; Resists; Alternative noise clustering; Fuzzy clustering; Noise clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Acquisition, 2006 IEEE International Conference on
  • Conference_Location
    Weihai
  • Print_ISBN
    1-4244-0528-9
  • Electronic_ISBN
    1-4244-0529-7
  • Type

    conf

  • DOI
    10.1109/ICIA.2006.305877
  • Filename
    4097810