Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 44, Issue 4, Pages 625-638Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0167-9473(02)00280-3
Keywords
minimum covariance determinant; robust clustering; outlier detection
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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/similar todmrocke/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. (C) 2002 Elsevier B.V. All rights reserved.
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