Skip to main content
SpringerLink
Log in

A Critical Approach to Non-Parametric Classification of Compositional Data

  • Conference paper
Advances in Data Science and Classification

Abstract

The application of hierarchic methods of classification needs to establish in advance some or all of the following measures: difference, central tendency and dispersion, in accordance with the nature of the data. In this work, we present the requirements for these measures when the data set to classify is a compositional data set. Specific measures of difference, central tendency and dispersion are defined to be used with the most usual non-parametric methods of classification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Aitchison, J., (1986). The Statistical Analysis of Compositional Data. Chapman and Hall, New York (USA), 416 p.

    Book  Google Scholar 

  • Aitchison, J., (1992). On Criteria for Measures of Compositional Difference, Mathematical Geology, vol. 24, No. 4, pp. 365–379

    Article  Google Scholar 

  • Aitchison, J., (1997). The one-hour course in compositional data analysis or compositional data analysis is simple, in: Proceedings of IAMG’97 - The 1997 Annual Conference of the International Association for Mathematical Geology, Ed. Pawlowsky-Glahn, V., CIMNE, Barcelona (E), Part I, pp. 3–35.

    Google Scholar 

  • Everitt, BS., (1993). Cluster Analysis. Edward Arnold, Cambridge (UK), 170 p.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. d’Informàtica i Matemàtica Aplicada, Escola Politècnica Superior, Universitat de Girona, Lluis Santaló, s/n, E-17071, Girona, Spain

    J. A. Martín-Fernández & C. Barceló-Vidal

  2. Dept. de Matemàtica Aplicada III, ETS de Eng. de Camins, Canals i Ports, Universitat Politècnica de Catalunya, Jordi Girona Salgado, 1 i 3, E-08034, Barcelona, Spain

    V. Pawlowsky-Glahn

Authors
  1. J. A. Martín-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Barceló-Vidal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. Pawlowsky-Glahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Statisticà, Probabilità e Statistiche Applicate, Università di Roma “La Sapienza”, Piazzale Aldo Moro 5, I-00185, Roma, Italia

    Alfredo Rizzi

  2. Dipartimento di Metodi Quantitativi e Teoria Economica, Università “G. D’Annunzio” di Chieti, Viale Pindaro 42, I-65127, Pescara, Italia

    Maurizio Vichi

  3. Institut für Statistik und Wirtschaftsmathematik, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Wüllnerstraße 3, D-52056, Aachen, Germany

    Hans-Hermann Bock

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin · Heidelberg

About this paper

Cite this paper

Martín-Fernández, J.A., Barceló-Vidal, C., Pawlowsky-Glahn, V. (1998). A Critical Approach to Non-Parametric Classification of Compositional Data. 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_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-72253-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64641-9

  • Online ISBN: 978-3-642-72253-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation