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Warianty tytułu
Języki publikacji
Abstrakty
Fuzzy and possibilistic clustering helps to find natural vague boundaries in data and has long been a popular unsupervised learning method. 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 sensitivity to the presence of noise and outliers in data. The FCM applies the constraint that the memberships of each datum across groups sum to 1. Due to this constraint and L/sub 2/ norm as the dissimilarity measure, the FCM has considerable trouble in a noisy environment. In possibilistic C-means (PCM) the above constraint is not used. In this case membership values may be interpreted as degrees of possibility that the datum belongs to the groups. In the possibilistic approach still L/sub 2/ norm is usually used and the second reason of sensitivity for outliers and noise remains. This paper introduces a new epsilon -insensitive Possibilistic C-Means ( epsilon PCM) clustering algorithm. The performance of the new clustering algorithm is experimentally compared with the PCM method using simple two-dimensional synthetic data with outliers and the real-world Iris database.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
141--155
Opis fizyczny
Bibliogr. 16 poz., tab., wzory
Twórcy
autor
- Institute of Electronics, Technical University of Silesia, Gliwice
Bibliografia
- [1] B. Anderson:The irises of the gaspe peninsula. Bull. Amer. IRIS Soc.,59 (1935), 2-5.
- [2] J. C. Bezdek: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1982.
- [3] R. N. Dave: Characterization and Detection of Noise in Clustering. Pattern Recognition Lett, 12(11), (1991), 657-664.
- [4] R. N. Dave and R. R. Kishnapuram: Robust Clustering Methods: A Unified View. IEEE Trans. Fuzzy Systems, 5(2), (1997), 270-293.
- [5] R. O. Duda and P. E. Hart: Pattern Classification and Scene Analysis. John Wiley & Sons, New York, 1973.
- [6] J. C. Dunn: A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well-Separated Cluster. Journal Cybernetics, 3(3), (1973), 32-57.
- [7] K. Fukunaga: Introduction lo Statistical Pattern Recognition. Academic Press, San Diego, 1990.
- [8] R. J. Hathaway and J. C. Bezdek: Generalized Fuzzy c-Means Clustering Strategies Using Lp Norm Distances. IEEE Trans. Fuzzy Systems. 8(5), (2000), 576-582.
- [9] P. J. Huber: Robust statistics. Wiley, New York, 1981.
- [10] K. Jajuga: L1-norm based fuzzy Clustering. Fuzzy Sets and Systems, 39(1), (1991), 43-50.
- [11] P. R. Kersten: Fuzzy Order Statistics and Their Application to Fuzzy Clustering. IEEE Trans. Fuzzy Systems, 7(6), (1999), 708-712.
- [12] R. Krishnapuram and J. M. Keller: A Possibilistic Approach to Clustering. IEEE Trans. Fuzzy Systems, 1(1), (1993), 98-110.
- [13] E. H. Ruspini: A New Approach to Clustering. Inform. Control 15(1), (1969), 22-32.
- [14] J. T. Tou and R. C. Gonzalez: Pattern Recognition Principles. Adison-Wesley, London, 1974.
- [15] V. Vapnik: Statistical Learning Theory. Wiley, New York, 1998.
- [16] L. A. Zadeh: Fuzzy Sets. Information and Control, 8 (1965), 338-353.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW9-0009-1667