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A Non-linear Variant of Global Clustering Using Kernel Methods

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2010, 15(4), pp.11-18
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

허경용 1 김성훈 2 Woo, Young Woon 3

1University of Florida
2경북대학교
3동의대학교

Accredited

ABSTRACT

Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.

Citation status

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