Abstract
The evaluation of the effectiveness of organisations can be aided by the use of cluster analysis, suggesting and clarifying differences in structure between successful and failing organisations. Unfortunately, traditional methods of cluster analysis are highly sensitive to the presence of atypical observations and departures from normality. We describe a form of robust clustering using the forward search that allows the data to determine the number of clusters and so allows for outliers. An example is given of the successful clustering of customers of a bank into groups that are decidedly non-normal.
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References
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Acknowledgements
This work was supported by the grants “Metodi statistici multivariati per la valutazione integrata della qualità dei servizi di pubblica utilità: efficacia-efficienza, rischio del fornitore, soddisfazione degli utenti” and “Metodologie statistiche per lanalisi di impatto e la valutazione della regolamentazione” of Ministero dell’Università e della Ricerca PRIN 2006.
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Atkinson, A.C., Riani, M., Cerioli, A. (2010). Robust Clustering for Performance Evaluation. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_43
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DOI: https://doi.org/10.1007/978-3-642-03739-9_43
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