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Exploratory tools for clustering multivariate data

Authors :
Marco Riani
Anthony C. Atkinson
Source :
Computational Statistics & Data Analysis. 52:272-285
Publication Year :
2007
Publisher :
Elsevier BV, 2007.

Abstract

The forward search provides a series of robust parameter estimates based on increasing numbers of observations. The resulting series of robust Mahalanobis distances is used to cluster multivariate normal data. The method depends on envelopes of the distribution of the test statistics in forward plots. These envelopes can be found by simulation; flexible polynomial approximations to the envelopes are given. New graphical tools provide methods not only of detecting clusters but also of determining their membership. Comparisons are made with mclust and k-means clustering.

Details

ISSN :
01679473
Volume :
52
Database :
OpenAIRE
Journal :
Computational Statistics & Data Analysis
Accession number :
edsair.doi...........657c32f102c839e19875d9df6fe609e4
Full Text :
https://doi.org/10.1016/j.csda.2006.12.034