Skip to main content
SpringerLink
Log in

Grade Correspondence-cluster Analysis Applied to Separate Components of Reversely Regular Mixtures

  • Conference paper
Classification, Clustering, and Data Analysis

  • 1760 Accesses

Abstract

The paper presents how the method called grade correspondence-cluster analysis (GCCA) can extract data subtables, which are characterized by specifically regular, distinctly different data structures. A short review of basic ideas underlying GCCA is given in Sec. 1. A description of straight and reverse regularity of data tables transformed by the GCCA is given in Sec. 2. These concepts are illustrated on a real data example, which describes development factors and economic status of Polish small business service firms. In the next sections, this data table is analyzed and effects of the method are demonstrated.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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.

References

  • CIOK, A. (1998): Discretization as a tool in cluster analysis. In: A. Rizzi, M. Vichi, and IL-H. Bock (Eds.): Advances in Data Science and Classification. Springer, Heidelberg, 349–354.

    Chapter  Google Scholar 

  • CIOK, A., KOWALCZYK, T., PLESZCZYNSKA, E., and SZCZESNY, W. (1995): Algorithms of grade correspondence-cluster analysis. The Collected Papers of Theoretical and Applied Computer Science, 7, no 1–4, 5–22.

    Google Scholar 

  • CIOK, A. (2001): Significance testing in the grade correspondence analysis. In: M. A. Klopotek, M. Michalewicz, and S. T. Wierzchon (Eds.): Intelligent Information Systems 2001, Physica-Verlag, Heidelberg, 67–74.

    Google Scholar 

  • PATEFIELD, W. M. (1981): Algorithm AS159. An efficient method of generating random RxC tables with given row and column totals. Applied Statistics, 30, 91–97.

    Article  MATH  Google Scholar 

  • NELSEN, R.B. (1991): Copulas and association. In: G. Dall’Aglio, S. Kotz, G. Salinetti (Eds.): Advances in Probability Distributions with Given Marginals. Kluwer Academic Publishers, Dordrecht.

    Google Scholar 

  • SCHRIEVER B. F. (1985): Order dependence, Ph.D. Dissertation, Free University of Amsterdam.

    Google Scholar 

  • SZCZESNY, W. (2001): Grade correspondence analysis applied to contingency tables and questionnaire data. Intelligent Data Analysis, 5, 1–35.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science PAS, ul. Ordona 21, 01-237, Warsaw, Poland

    Alicja Ciok

  2. Institute of Home Market and Consumption, ul. Al. Jerozolimskie 87, 02-001, Warsaw, Poland

    Alicja Ciok

Authors
  1. Alicja Ciok
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics, ul. Komandorska 118/120, 53-345, Wroclaw, Poland

    Krzysztof Jajuga

  2. Department of Statistics, Cracow University of Economics, ul. Rakowicka 27, 31-510, Cracow, Poland

    Andrzej Sokołowski

  3. Institute of Statistics, Technical University of Aachen, Wuellnerstrasse 3, 52056, Aachen, Germany

    Hans-Hermann Bock

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ciok, A. (2002). Grade Correspondence-cluster Analysis Applied to Separate Components of Reversely Regular Mixtures. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-56181-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43691-1

  • Online ISBN: 978-3-642-56181-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation