Robust Clustering Algorithms Based on Finite Mixtures of Multivariate t Distribution
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 . Experiment results show the contrast among different algorithms and demonstrate the effectiveness of our algorithms.
KeywordsFinite Mixture Finite Mixture Model Mixture Normal Model Deterministic Annealing Minimum Message Length
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