Gradient Based Fuzzy C-Means Algorithm with a Mercer Kernel

  • Dong-Chul Park
  • Chung Nguyen Tran
  • Sancho Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, a clustering algorithm based on Gradient Based Fuzzy C-Means with a Mercer Kernel, called GBFCM (MK), is proposed. The kernel method adopted in this paper implicitly performs nonlinear mapping of the input data into a high-dimensional feature space. The proposed GBFCM(MK) algorithm is capable of dealing with nonlinear separation boundaries among clusters. Experiments on a synthetic data set and several real MPEG data sets show that the proposed algorithm gives better classification accuracies than both the conventional k-means algorithm and the GBFCM.


Feature Space Kernel Method Membership Grade Code Vector Gaussian Kernel Function 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-Chul Park
    • 1
  • Chung Nguyen Tran
    • 1
  • Sancho Park
    • 2
  1. 1.Dept. of Information EngineeringMyong Ji UniversityKorea
  2. 2.Davan Tech Co.SeongnamKorea

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