Abstract
Suppose that we are given a list of n ℝp-valued observations and a natural number r ≤ n. Further, assume that r of them arise from any one of g normally distributed populations, whereas the other n — r observations are assumed to be contaminations. We develop estimators which simultaneously detect n — r outliers and partition the remaining r observations in g clusters. We analyze under which conditions these estimators are maximum likelihood estimators. Finally, we propose algorithms that approximate these estimators.
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Gallegos, M.T. (2002). Maximum Likelihood Clustering with Outliers. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_27
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DOI: https://doi.org/10.1007/978-3-642-56181-8_27
Publisher Name: Springer, Berlin, Heidelberg
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