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 (http://www.cipic.ucdavis.edu/,,,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.