Abstract
One of the most important problems among the methodological issues discussed in cluster analysis is the identification of the correct number of clusters and the correct allocation of units to their natural clusters. In this paper we use the forward search algorithm, recently proposed by Atkinson, Riani and Cerioli (2004) to scrutinize in a robust and efficient way the output of k-means clustering algorithm. The method is applied to a data set containing efficiency and effectiveness indicators, collected by the National University Evaluation Committee (NUEC), used to evaluate the performance of Italian universities.
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Bini, M. (2005). Robust Multivariate Methods for the Analysis of the University Performance. In: Bock, HH., et al. New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27373-5_34
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DOI: https://doi.org/10.1007/3-540-27373-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23809-6
Online ISBN: 978-3-540-27373-8
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