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A Comparison between Two Fuzzy Clustering Algorithms for Mixed Features

  • Irene Olaya Ayaquica-Martínez
  • José F. Martínez-Trinidad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

In this paper, a comparative analysis of the mixed-type variable fuzzy c-means (MVFCM) and the fuzzy c-means using dissimilarity functions (FCMD) algorithms is presented. Our analysis is focused in the dissimilarity function and the way of calculating the centers (or representative objects) in both algorithms.

Keywords

Cluster Center Membership Degree Comparison Criterion Representative Object Mixed Feature 
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.

References

  1. 1.
    Schalkoff, R.J.: Pattern Recognition: Statistical, Structural and Neural approaches. John Wiley & Sons, Inc., USA (1992)Google Scholar
  2. 2.
    Yang, M.S., Hwang, P.Y., Chen, D.H.: Fuzzy Clustering algorithms for mixed feature variables. Fuzzy Sets and Systems (2003) (in Press)Google Scholar
  3. 3.
    Ayaquica, M.I., Martínez, T.J.F.: Fuzzy c-means algorithm to analyze mixed data. In: VI Iberamerican Symp. on Pattern Recognition, Florianopolis, Brazil, pp. 27–33 (2001)Google Scholar
  4. 4.
    Ayaquica, M.I.: Fuzzy c-means algorithm using dissimilarity functions. Thesis to obtain the Master Degree. Center for Computing Research, IPN, Mexico (2002) (in Spanish)Google Scholar
  5. 5.
    Gowda, K.C., Diday, E.: Symbolic clustering using a new dissimilarity measure. Pattern Recognition 24(6), 567–578 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Irene Olaya Ayaquica-Martínez
    • 1
  • José F. Martínez-Trinidad
    • 1
  1. 1.Instituto Nacional de Astrofísica, Óptica y ElectrónicaSanta María TonantzintlaMéxico

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