Publication: Cluster identification using projections
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2001-12
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American Statistical Association
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Abstract
This artiele describes a procedure to identify elusters in multivariate data using information obtained from the univariate projections
of the sample data onto certain directions. The directions are chosen as those that minimize and maximize the kurtosis coefficlent of
the projected data. It is shown that, under certain conditions, these directions provide the largest separatlOn for the dlfferent clusters.
The projected univariate data are used to group the observations according to the values of the gaps or spacings between consecutive-ordered
observations. These groupings are then combined over all projection directions. The behavior of the method is tested on several
examples, and compared to k-means, MCLUST, and the procedure proposed by Jones and Sibson in 1987. The proposed algonthm is
iterative, affine equivariant, flexible, robust to outliers, fast to implement, and seems to work well in practice
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Keywords
Classification, Kurtosis, Multivariate analysis, Robustness, Spacings
Bibliographic citation
Journal of the American Statistical Association, 2001, v. 96, n. 456, p. 1433-1445