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The Geometric Approach to the Comparison of Multivariate Time Trajectories

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Advances in Classification and Data Analysis

Abstract

To compare time trajectories different approaches might be envisaged. In this paper, considering the geometric approach, several dissimilarity measures between time trajectories are taken into account. An empirical comparison of the dissimilarity measures is also shown.

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References

  • Carlier, A. (1986) Factor Analysis of Evolution and Cluster Methods on Trajectories, COMPSTAT, Physics-Verlag, 140 – 145.

    Google Scholar 

  • Carlier, A. (1991) About Distances for Clustering Longitudinal Multivariate Data, in Third Conference of the IFCS, Edinburgh, Scotland.

    Google Scholar 

  • Coppi, R. (1998) Lectures in Multivariate Analysis, Dipartimento di Statistics, Probabilità e St. Appl., Università di Roma ‘La Sapienza’, Serie C, in Italian.

    Google Scholar 

  • D’Urso, P., Gastaldi, T. (1999) Classification of Time Trajectories Based on A ‘Polygonal’ Dissimilarity Measure, submitted.

    Google Scholar 

  • D’Urso, P., Vichi, M. (1998) Dissimilarities Between Trajectories of a Three-Way Longitudinal Data Set, in: Advances in Data Science and Classification, Rizzi A., Vichi M., Bock, H. H. (Eds.), Springer-Verlag, 585 – 592.

    Google Scholar 

  • Gordon, A.D. (1990) Constructing Dissimilarity Measures, J. of Class., 7, 257 – 269.

    Article  Google Scholar 

  • Joly, S., Le Calvé, G. (1995) Three-Way Distances, J. of Class., 12, 191 - 205.

    Article  Google Scholar 

  • Kosmelj, K., Batagelj, V. (1990) Cross-Sectional Approach for Clustering Time Varying Data, Journal of Classification, 7, 99 - 109.

    Article  Google Scholar 

  • Saporta, G., Lavallard, F. (1996) L’Analyse des Données Évolutives. Méthodes et Applications. Éditions Technip, Paris.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Statistica, Probabilà e Statistiche Applicate, Università di Roma “La Sapienza”, Roma, Italy

    Pierpaolo D’Urso

Authors
  1. Renato Coppi
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  2. Pierpaolo D’Urso
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Editor information

Editors and Affiliations

  1. Department SEFEMEQ, University of Rome “Tor Vergata”, Via di Tor Vergata, 00133, Rome, Italy

    Simone Borra

  2. Department SEGeS, University of Molise, Via de Sanctis, 86100, Campobasso, Italy

    Roberto Rocci

  3. Department Statistica, Probabilità e Statistiche Applicate, University of Rome “La Sapienza”, Piazza le Aldo Moro, 00185, Rome, Italy

    Maurizio Vichi

  4. University of Mannheim, 68131, Mannheim, Germany

    Martin Schader (Lehrstuhl für Wirtschaftsinformatik III Schloß) (Lehrstuhl für Wirtschaftsinformatik III Schloß)

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© 2001 Springer-Verlag Berlin Heidelberg

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Coppi, R., D’Urso, P. (2001). The Geometric Approach to the Comparison of Multivariate Time Trajectories. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-59471-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41488-9

  • Online ISBN: 978-3-642-59471-7

  • eBook Packages: Springer Book Archive

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