Abstract
In this paper we address some issues in the field of cluster stability. In particular, we study the effect of deleting individual cases and variables on the results of a (nonhierarchical) cluster analysis. We do not restrict to computation of a single influence measure for each data point, or variable, but we analyze how individual influence varies when the number of clusters changes. For this purpose we suggest the use of simple deletion diagnostics computed by cross-validation. The suggested approach is applied to real data and results are displayed by means of a simple tool of modern multivariate-data visualization. Furthermore, the performance of our diagnostics is assessed through Monte Carlo simulations both under the null hypothesis of well-behaved data and the alternative hypothesis of isolated contamination.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Cerioli, A. (1999). Measuring the Influence of Individual Observations and Variables in Cluster Analysis. In: Vichi, M., Opitz, O. (eds) Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60126-2_1
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DOI: https://doi.org/10.1007/978-3-642-60126-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65633-3
Online ISBN: 978-3-642-60126-2
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