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 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 F distribution method for multiple clusters which have different sizes and shapes.