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Open Access Publications from the University of California

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


Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robust multivariate location and scale estimator can be used to find outlying points. Hardin and Rocke (,,,dmrocke/preprints.htrnl)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 F distribution method for multiple clusters which have different sizes and shapes.

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