Introduces a robust fuzzy clustering approach for cellwise outliers that leverages cluster-specific variable relationships to detect and correct anomalous cells while controlling assignment fuzziness.
As also for the first two variables, the choice of α = 0.05, which corresponds to the true level of contamination in the data, is confirmed by the ∆ plots
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Robust fuzzy clustering with cellwise outliers
Introduces a robust fuzzy clustering approach for cellwise outliers that leverages cluster-specific variable relationships to detect and correct anomalous cells while controlling assignment fuzziness.