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

  • Author(s): Hardin, Johanna
  • Rocke, David
  • et al.
Abstract

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.

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