Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator

Johanna Hardin; David M Rocke

doi:10.1016/S0167-9473(02)00280-3

Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator

Johanna Hardin, David M Rocke

Public Health Sciences

Research output: Contribution to journal › Article › peer-review

96 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	625-638
Number of pages	14
Journal	Computational Statistics and Data Analysis
Volume	44
Issue number	4
DOIs	https://doi.org/10.1016/S0167-9473(02)00280-3
State	Published - Jan 28 2004

Keywords

Minimum covariance determinant
Outlier detection
Robust clustering

ASJC Scopus subject areas

Computational Theory and Mathematics
Statistics, Probability and Uncertainty
Electrical and Electronic Engineering
Computational Mathematics
Numerical Analysis
Statistics and Probability

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Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator

Abstract

Keywords

ASJC Scopus subject areas

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