Outlier Detection in the Multiple Cluster Setting Using the Minimum Covariance Determinant Estimator

Document Type



Mathematics (Pomona)

Publication Date



Minimum covariance determinant, Robust clustering, Outlier detection


Mahalanobis-type distances in which the shape matrix is derived from a consistent high-breakdown robust multivariate location and scale estimator can be used to find outlying points. Hardin and Rocke (http://www.cipic.ucdavis.edu/~dmrocke/preprints.html) developed a new method for identifying outliers in a one-cluster setting using an F distribution. We extend the method to the multiple cluster case which gives a robust clustering method in conjunction with an outlier identification method. We provide results of the Fdistribution method for multiple clusters which have different sizes and shapes.

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© 2002 Elsevier B.V.

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