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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BUS8-0020-0091

Czasopismo

Fundamenta Informaticae

Tytuł artykułu

A Novel Multimodal Probability Model for Cluster Analysis

Autorzy Yu, J.  Yang, M-S.  Hao, P. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN 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
EN cluster analysis   probability density function  
Wydawca IOS Press
Czasopismo Fundamenta Informaticae
Rocznik 2011
Tom Vol. 111, nr 1
Strony 81--90
Opis fizyczny Bibliogr. 16 poz., wykr.
Twórcy
autor Yu, J.
autor Yang, M-S.
autor Hao, P.
  • Dept. of Computer Science, Beijing Jiaotong University, Beijing, 100044, China, jianyu@bjtu.edu.cn
Bibliografia
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[9] McLachlan, G.J. , Basford, K.E.: Mixture Models: Inference and Applications to clustering. Marcel Dekker, New York, 1988.
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[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.
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