Alternative Fuzzy Clustering Algorithms with L1-Norm and Covariance Matrix

  • Miin-Shen Yang
  • Wen-Liang Hung
  • Tsiung-Iou Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the best known and most used method. Although FCM is a very useful method, it is sensitive to noise and outliers so that Wu and Yang (2002) proposed an alternative FCM (AFCM) algorithm. In this paper, we consider the AFCM algorithms with L1-norm and fuzzy covariance. These generalized AFCM algorithms can detect elliptical shapes of clusters and also robust to noise and outliers. Some numerical experiments are performed to assess the performance of the proposed algorithms. Numerical results clearly indicate the proposed algorithms to be superior to the existing methods.


Covariance Matrix Cluster Algorithm Cluster Result Fuzzy Cluster Heavy Tail Distribution 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miin-Shen Yang
    • 1
  • Wen-Liang Hung
    • 2
  • Tsiung-Iou Chung
    • 1
  1. 1.Department of Applied MathematicsChung Yuan Christian UniversityChung-LiTaiwan
  2. 2.Department of Applied MathematicsNational Hsinchu University of EducationHsin-ChuTaiwan

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