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.
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Schalkoff, R.J.: Pattern Recognition: Statistical, Structural and Neural approaches. John Wiley & Sons, Inc., USA (1992)
Yang, M.S., Hwang, P.Y., Chen, D.H.: Fuzzy Clustering algorithms for mixed feature variables. Fuzzy Sets and Systems (2003) (in Press)
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)
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)
Gowda, K.C., Diday, E.: Symbolic clustering using a new dissimilarity measure. Pattern Recognition 24(6), 567–578 (1991)
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Ayaquica-Martínez, I.O., Martínez-Trinidad, J.F. (2003). A Comparison between Two Fuzzy Clustering Algorithms for Mixed Features. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds) Progress in Pattern Recognition, Speech and Image Analysis. CIARP 2003. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24586-5_58
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DOI: https://doi.org/10.1007/978-3-540-24586-5_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20590-6
Online ISBN: 978-3-540-24586-5
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