Abstract
During a forward search the plot of minimum Mahalanobis distances of observations not in the subset provides a test for outliers. However, if clusters are present in the data, their simple identification requires that there arc searches that initially include a preponderance of observations from each of the unknown clusters. We use random starts to provide such searches, combined with simulation envelopes for precise inference about clustering.
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References
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© 2006 Springer-Verlag Heidelberg
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Atkinson, A., Riani, M., Cerioli, A. (2006). Random Start Forward Searches with Envelopes for Detecting Clusters in Multivariate Data. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_19
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DOI: https://doi.org/10.1007/3-540-35978-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35977-7
Online ISBN: 978-3-540-35978-4
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