Epsilon-Insensitive Fuzzy c-Medians Clustering

• Autorzy: Łęski J.
• Języki publikacji: EN

Abstrakty

EN

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy C-Means (FCM) 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 sensivity to presence of noise and outliers in data. This paper introduces a new ε-insensitive Fuzzy C-Medians (εFCMed) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMed). Performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) and the FCMed methods using synthetic heavy-tailed and overlapped groups in background noise, and the Iris database.

Słowa kluczowe

EN

fuzzy clustering; fuzzy c-medians; epsilon-intensivity; robust methods

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2002
• Tom: Vol. 50, Nr 4
• Strony: 361--374
• Opis fizyczny: Bibliogr. 24 poz., 4 rys., 3 tab.

Twórcy

autor

Łęski J.

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland (Instytut Elektroniki Politechniki Śląskiej)

Bibliografia

• [1] R. O. Duda, P. E. Hart, Pattern classification and scene analysis, John Wiley & Sons, New York 1973.
• [2] B.S. Everitt, Cluster analysis, Arnold, London 1993.
• [3] K. Fukunaga, Introduction to statistical pattern recognition, Academic Press, San Diego 1990.
• [4] J.T. Tou, R.C. Gonzalez, Pattern recognition principles, Adison-Wesley, London 1974.
• [5] L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965) 338-353.
• [6] E. H. Ruspini, A new approach to clustering, Inform. Control, 15 (1969) 22-32.
• [7] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated cluster, Journal Cybernetics, 3 (1973) 32-57.
• [8] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York 1982.
• [9] R. Krishnapuram, J.M. Keller, A possibilistic approach to clustering, IEEE Trans. Fuzzy Systems, 1 (1993) 98-110.
• [10] R. N. Davé, Characterization and detection of noise in clustering, Pattern recognition Lett., 12 (1991) 657-664.
• [11] R. J. Hathaway, J.C. Bezdek, Generalized fuzzy c-means clustering strategies using Lp norm distances, IEEE Trans. Fuzzy Systems, 8 (2000) 576-582.
• [12] K. Jajuga, L1-norm based fuzzy clustering, Fuzzy Sets and Systems, 39 (1991) 43-50.
• [13] P.R. Kersten, Fuzzy order statistics and their application to fuzzy clustering, IEEE Trans. Fuzzy Systems, 7 (1999) 708-712.
• [14] P. J. Huber, Robust statistics, Wiley, New York 1981.
• [15] R.N. Davé, R. Krishnapuram, Robust clustering methods: a unified view, IEEE Trans. Fuzzy Systems, 5 (1997) 270-293.
• [16] V. Vapnik, Statistical learning theory, Wiley, New York 1998.
• [17] Y.-C. Ho, R.L. Kashyap, An algorithm for linear inequalities and its applications, IEEE Trans. Elec. Comp., 14 (1965) 683-688.
• [18] Y.-C. Ho, R.L. Kashyap,A class of iterative procedures for linear inequalities, JSSIAM Control, 4 (1966) 112-115.
• [19] E. Anderson, The irises of the gaspe peninsula, Bull. Amer. IRIS Soc, 59 (1935) 2-5.
• [20] A.M. Beinsed, L.O. Hall, J.C. Bezdek, L.P. Clarke, M.L. Silbiger, J. A. Arrington, R.F. Murtagh, Validity-Guided (Re)clustering with application to image segmentation, IEEE Trans. Fuzzy Systems, 4 (1996) 112-123.
• [21] J. Leski, Ordered weighted generalized conditional possibilistic clustring, in: Fuzzy sets and their applications, ed.: J. Chojcan, J. Leski, Silesian University Press, Gliwice (2001) 469-479.
• [22] N.B. Karayiannis, Weighted fuzzy learning vector quantization and weighted generalized fuzzy c-means algorithms, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, IEEE Press, New York (1996) 1036-1042.
• [23] N.R. Pal, K. Pal, J.C. Bezdek, A mized c-means clustering model, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, IEEE Press, New York (1997) 11-22.
• [24] N.B. Karayiannis, Soft learning vector quantization and clustering algorithms based on reformulation, Proceedings of the Seventh IEEE International Conference on Fuzzy Systems, IEEE Press, New York (1998) 1441-1446.