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- W1802250012 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." @default.
- W1802250012 created "2016-06-24" @default.
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- W1802250012 date "2006-01-01" @default.
- W1802250012 modified "2023-09-26" @default.
- W1802250012 title "Robust Clustering Algorithms Based on Finite Mixtures of Multivariate t Distribution" @default.
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- W1802250012 doi "https://doi.org/10.1007/11881070_83" @default.
- W1802250012 hasPublicationYear "2006" @default.
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