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Fuzzy C-Means Algorithm with Divergence-Based Kernel

  • Young-Soo Song
  • Dong-Chul Park
  • Chung Nguyen Tran
  • Hwan-Soo Choi
  • Minsoo Suk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

A Fuzzy C-Means algorithm with a Divergence-based Kernel (FCMDK) for clustering Gaussian Probability Density Function (GPDF) data is proposed in this paper. The proposed FCMDK is based on the Fuzzy C-Means algorithm and employs a kernel method for data transformation. The kernel method adopted in the proposed FCMDK is used to transform input data into a feature space of a higher dimensionality so that the nonlinear problems residing in input space can be linearly solved in the feature space. In order to deal with GPDF data, a divergence-based kernel employing a divergence distance measure for its similarly measure is used for data transformation. The proposed FCMDK is used for clustering GPDF data in an image classification model. Experiments and results on Caltech data sets demonstrate that the proposed FCMDK is more efficient than other conventional algorithms.

Keywords

Feature Space Discrete Cosine Transform Gaussian Mixture Model Kernel Method Membership Grade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Young-Soo Song
    • 1
  • Dong-Chul Park
    • 1
  • Chung Nguyen Tran
    • 1
  • Hwan-Soo Choi
    • 1
  • Minsoo Suk
    • 2
  1. 1.Dept. of Information EngineeringMyong Ji UniversityKorea
  2. 2.School of Info. and Comm. Eng.Sungkyunkwan UniversityKorea

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