Robust clustering algorithms based on finite mixtures of multivariate t distribution

Authors:
Chengwen Yu;Qianjin Zhang;Lei Guo
Affiliations:
College of Automatic Control, Northwestern Polytechnical University, Xi'an, China;College of Automatic Control, Northwestern Polytechnical University, Xi'an, China;College of Automatic Control, Northwestern Polytechnical University, Xi'an, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 4
Cited 1

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Robust mixture modelling using the t distribution

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Robust Cluster Analysis via Mixtures of Multivariate t-Distributions

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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.