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2001 | 11 | 4 | 993-1007
Tytuł artykułu

An ε-insensitive approach to fuzzy clustering

Autorzy
Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
Rocznik
Tom
11
Numer
4
Strony
993-1007
Opis fizyczny
Daty
wydano
2001
otrzymano
2001-03-30
poprawiono
2001-07-04
Twórcy
  • Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
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  • Davé R.N. and Krishnapuram R. (1997): Robust clustering methods: A unified view. — IEEE Trans. Fuzzy Syst., Vol.5, No.2, pp.270–293.
  • Duda R.O. and Hart P.E. (1973): Pattern Classification and Scene Analysis. — New York: Wiley.
  • Dunn J.C. (1973): A fuzzy relative of the ISODATA process and its use in detecting compact well-separated cluster. — J. Cybern., Vol.3, No.3, pp.32–57.
  • Fukunaga K. (1990): Introduction to Statistical Pattern Recognition. — San Diego: Academic Press.
  • Hathaway R.J. and Bezdek J.C. (2000): Generalized fuzzy c-means clustering strategies using Lp norm distances. — IEEE Trans. Fuzzy Syst., Vol.8, No.5, pp.576–582.
  • Huber P.J. (1981): Robust statistics. — New York: Wiley.
  • Jajuga K. (1991): L1 -norm based fuzzy clustering. — Fuzzy Sets Syst., Vol.39, No.1, pp.43– 50.
  • Kersten P.R. (1999): Fuzzy order statistics and their application to fuzzy clustering. — IEEE Trans. Fuzzy Syst., Vol.7, No.6, pp.708–712.
  • Krishnapuram R. and Keller J.M. (1993): A possibilistic approach to clustering. — IEEE Trans. Fuzzy Syst., Vol.1, No.1, pp.98–110.
  • Pal N.R. and J.C. Bezdek (1995): On cluster validity for the fuzzy c-means model. — IEEE Trans. Fuzzy Syst., Vol.3, No.3, pp.370–379.
  • Ruspini E.H. (1969): A new approach to clustering. — Inf. Contr., Vol.15, No.1, pp.22–32.
  • Tou J.T. and Gonzalez R.C. (1974): Pattern Recognition Principles. — London: Addison-Wesley.
  • Vapnik V. (1998): Statistical Learning Theory. — New York: Wiley.
  • Zadeh L.A. (1965): Fuzzy sets. — Inf. Contr., Vol.8, pp.338–353.
Typ dokumentu
Bibliografia
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bwmeta1.element.bwnjournal-article-amcv11i4p993bwm
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