Abstract
The problem of clustering of multivariate random data is considered in presence of outliers. The hypothetical model of data is described by a mixture of regular m-parametric probability densities. Clustering of data is made by the often used in practice decision rule which is derived by substitution of ML-estimators (on the unclassified sample) of parameters for their unknown true values in Bayesian decision rule. Robustness of probability of classification error is evaluated. The new clustering algorithm with smoothing is presented. Illustration for the case of the Gaussian hypothetical model and for the Fisher's data under outliers is given.
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© 1997 Springer-Verlag
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Kharin, Y. (1997). Robustness of clustering under outliers. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052866
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DOI: https://doi.org/10.1007/BFb0052866
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