Czasopismo
2011
|
Vol. 111, nr 1
|
81-90
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classification maximum likelihood method, to use probabilitymodels for cluster analysis. However, the corresponding probability distributions to most clustering algorithms such as fuzzy c-means, possibilistic c-means, mode-seeking methods, etc., have not yet been found. In this paper, we construct a multimodal probability distribution model and then present the relationships between many clustering algorithms and the proposed model via the maximum likelihood estimation. Moreover, we also give the theoretical properties of the proposed multimodal probability distribution.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
81-90
Opis fizyczny
Bibliogr. 16 poz., wykr.
Twórcy
autor
autor
autor
- Dept. of Computer Science, Beijing Jiaotong University, Beijing, 100044, China, jianyu@bjtu.edu.cn
Bibliografia
- [1] Bezdek,J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. New York:PlenumPress, 1981.
- [2] Bryant, P.G. , Williamson, J.A. : Asymptotic behavior of classification maximum likelihood estimates. Biometrica, Vol. 65,273-438, 1978.
- [3] Celeux, G. , Govaert,G. : Clustering criteria for discrete data and latent classmodels. Journal of classification, Vol. 8, 157-176, 1991.
- [4] Fukunaga, K. , Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Information Theory, 21, 32-40, 1975.
- [5] Kaufman, L. , Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York, 1990.
- [6] Krishnapuram, R. , Keller, J.M.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Systems, 1, 98-110, 1993.
- [7] Lloyd, S.: Least squares quantization in pcm. Bell Telephone Laboratories Papers, Marray Hill, 1957.
- [8] MacQueen, J.: Some methods for classification and analysis of multivariate observations. Proc. of 5th Berkeley SymposiumonMathematical Statistics and Probability, Vol. 1,281-297, Berkley: University of California Press, 1967.
- [9] McLachlan, G.J. , Basford, K.E.: Mixture Models: Inference and Applications to clustering. Marcel Dekker, New York, 1988.
- [10] Scott, A.J. , Symons, M.J.: Clustering methods based on likelihood ration criteria. Biometrics, Vol. 27, 387-397, 1971.
- [11] Bock, H.H.: Probability models and hypotheses testing in partitioning cluster analysis. In: Arabie, P., Hubert, L.J., Soete, G.D. (eds.):Clustering and Classification, 377-453,World Scientific Publ., River Edge, NJ, 1996.
- [12] Yang, M.S.: On a class of fuzzy classification maximum likelihood procedures. Fuzzy Sets and Systems, 57, 365-375, 1993.
- [13] Yang, M.S. , Wu, K.L.: A similarity-based robust clustering method. IEEE Trans. Pattern Anal. Machine Intelligence 26, 434-448, 2004.
- [14] Yu, J.: General C-means clustering model. IEEE Trans. Pattern Anal. Machine Intelligence, 27(8), 1197-1211, Aug. 2005.
- [15] Windham, M.P. : Statistical models for cluster analysis. in: E. Diday, Y. Lechevallier (1991): Symbolicnumeric data analysis and learning, Commack, New York: Nova Science, 17-26.
- [16] Govaert, G.: Clustering model and metric with continuous data. in: E. Diday (1989): Learning symbolic and numeric knowledge, Commack, New York: Nova Science, 95-102.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0020-0091