Title

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

Document Type

Article

Department

Mathematics (Pomona)

Publication Date

2004

Keywords

Minimum covariance determinant, Robust clustering, Outlier detection

Abstract

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.

Rights Information

© 2002 Elsevier B.V.

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