• DocumentCode
    1813384
  • Title

    Clustering Algorithms Based on Mahalanobis Distances

  • Author

    Yih, Jeng-Ming ; Lin, Yuan-Horng

  • Author_Institution
    Dept. of Math. Educ., Nat. Taichung Univ., Taichung, Taiwan
  • fYear
    2010
  • fDate
    29-31 July 2010
  • Firstpage
    228
  • Lastpage
    231
  • Abstract
    Fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. In this paper, an improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance by taking a new threshold value and a new convergent process is proposed. The experimental results of three real data sets containing image classification show that our proposed new algorithm has the better performance.
  • Keywords
    fuzzy set theory; pattern classification; pattern clustering; Euclidean distance function; FCM; Mahalanobis distances; clustering algorithms; fuzzy c-means algorithm; image classification; spherical structural; threshold value; Accuracy; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Equations; Euclidean distance; Iris; Clustering Algorithms; FCM; Mahalanobis Distances;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Commerce and Security (ISECS), 2010 Third International Symposium on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4244-8231-3
  • Electronic_ISBN
    978-1-4244-8231-3
  • Type

    conf

  • DOI
    10.1109/ISECS.2010.57
  • Filename
    5557397
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1813384