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) |
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Pages (from-to) | 625-638 |
Number of pages | 14 |
Journal | Computational Statistics and Data Analysis |
Volume | 44 |
Issue number | 4 |
DOIs | |
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