Abstract
Providing protection against outlier in clustering data is a difficult problem. We proposed two robust clustering algorithms which integrate two modified versions of EM algorithm for mixtures t model with a model selection criterion respectively. The proposed methods can select the number of clusters component automatically by a combined component annihilation strategy and can also avoid the drawbacks of traditional mixture-based clustering algorithms – highly dependent on initialization and may converge to the boundary of the parameter space [7]. Experiment results show the contrast among different algorithms and demonstrate the effectiveness of our algorithms.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jain, A.K., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley & Sons, New York (2000)
McLachlan, G., Peel, D.: Robust Cluster Analysis via Mixtures of Multivariate t Distribution. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 658–666. Springer, Heidelberg (1998)
Peel, D., McLachlan, G.: J. Robust Mixture Modeling using the t Distribution, Statistics and Computing 10, 339–348 (2000)
Celeux, G., Chretien, S., Forbes, F., Mkhadri, A.: A Component Wise EM Algorithm for Mixtures. J. of Computational and Graphical Statistics 16(10), 697–712 (2001)
Liu, C., Rubin, D.B.: ML Estimation of the t Distribution using EM and its Extensions. ECM and ECME, Statistica Sinica (5), 19–39 (1995)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)
Bernardo, J., Smith, A.: Bayesian Theory. J. Wiley & Sons, Chichester (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, C., Zhang, Q., Guo, L. (2006). Robust Clustering Algorithms Based on Finite Mixtures of Multivariate t Distribution. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_83
Download citation
DOI: https://doi.org/10.1007/11881070_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)