Robust multivariate methods for the analysis of the university performance

Matilde Bini

doi:10.1007/3-540-27373-5_34

Robust multivariate methods for the analysis of the university performance

Matilde Bini

University of Florence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Studies in Classification, Data Analysis, and Knowledge Organization
Editors	Maurizio Vichi, Paola Monari, Stefania Mignani, Angela Montanari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	285-292
Number of pages	8
ISBN (Print)	9783319557076, 9783319557229, 9783540238096
DOIs	https://doi.org/10.1007/3-540-27373-5_34
State	Published - 1 Jan 2005
Externally published	Yes
Event	Biannual meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2003 - Bologna, Italy Duration: 22 Sep 2003 → 24 Sep 2003

Publication series

Name	Studies in Classification, Data Analysis, and Knowledge Organization
Volume	0
ISSN (Print)	1431-8814
ISSN (Electronic)	2198-3321

Conference

Conference	Biannual meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2003
Country/Territory	Italy
City	Bologna
Period	22/09/03 → 24/09/03

Access to Document

10.1007/3-540-27373-5_34

Cite this

Bini, M. (2005). Robust multivariate methods for the analysis of the university performance. In M. Vichi, P. Monari, S. Mignani, & A. Montanari (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization (pp. 285-292). (Studies in Classification, Data Analysis, and Knowledge Organization; Vol. 0). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/3-540-27373-5_34

Bini, Matilde. / Robust multivariate methods for the analysis of the university performance. Studies in Classification, Data Analysis, and Knowledge Organization. editor / Maurizio Vichi ; Paola Monari ; Stefania Mignani ; Angela Montanari. Springer Science and Business Media Deutschland GmbH, 2005. pp. 285-292 (Studies in Classification, Data Analysis, and Knowledge Organization).

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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 M Vichi, P Monari, S Mignani & A Montanari (eds), Studies in Classification, Data Analysis, and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization, vol. 0, Springer Science and Business Media Deutschland GmbH, pp. 285-292, Biannual meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2003, Bologna, Italy, 22/09/03. https://doi.org/10.1007/3-540-27373-5_34

Robust multivariate methods for the analysis of the university performance. / Bini, Matilde.
Studies in Classification, Data Analysis, and Knowledge Organization. ed. / Maurizio Vichi; Paola Monari; Stefania Mignani; Angela Montanari. Springer Science and Business Media Deutschland GmbH, 2005. p. 285-292 (Studies in Classification, Data Analysis, and Knowledge Organization; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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SN - 9783319557076

SN - 9783319557229

SN - 9783540238096

T3 - Studies in Classification, Data Analysis, and Knowledge Organization

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BT - Studies in Classification, Data Analysis, and Knowledge Organization

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A2 - Monari, Paola

A2 - Mignani, Stefania

A2 - Montanari, Angela

PB - Springer Science and Business Media Deutschland GmbH

T2 - Biannual meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2003

Y2 - 22 September 2003 through 24 September 2003

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Bini M. Robust multivariate methods for the analysis of the university performance. In Vichi M, Monari P, Mignani S, Montanari A, editors, Studies in Classification, Data Analysis, and Knowledge Organization. Springer Science and Business Media Deutschland GmbH. 2005. p. 285-292. (Studies in Classification, Data Analysis, and Knowledge Organization). doi: 10.1007/3-540-27373-5_34

Robust multivariate methods for the analysis of the university performance

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