Abstract
In this paper we propose a new approach to the exploratory analysis of multivariate clustered data. Our technique is based on a fast forward search algorithm which orders multivariate observations from those most in agreement with a specified clustering structure to those least in agreement with it. Simple graphical displays of a variety of statistics involved in the forward search lead to the identification of multiple outliers and influential observations in nonhierarchical cluster analysis, without being affected by masking and swamping problems. The suggested approach is applied to the convergent K-means method in two examples, both with real and simulated data.
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Cerioli, A. (1998). A New Method for Detecting Influential Observations in Nonhierarchical Cluster Analysis. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_2
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DOI: https://doi.org/10.1007/978-3-642-72253-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
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