PL EN


Journal
2013 | 3(41) | 32-39
Article title

Unfolding analysis adaptation for symbolic data – hybrid and symbolic-numeric approach

Content
Title variants
Languages of publication
EN
Abstracts
EN
The aim of this paper is to propose and present adaptations of unfolding analysis for symbolic data. In the article, the basic terms of unfolding analysis and symbolic data are presented. The paper presents two approaches – the internal hybrid approach and the external symbolic-numeric approach. In the empirical part, the external symbolic-numeric unfolding for LCD brands is presented. Symbolic multidimensional scaling R source codes were written by authors.
Contributors
  • Wroclaw University of Ecomomics
author
  • Wroclaw University of Ecomomics
References
  • Billard L., Diday E. (eds.), Symbolic Data Analysis: Conceptual Statistics and Data Mining, Wiley, Chichester 2006.
  • Bock H.H., Diday E. (eds.), Analysis of Symbolic Data. Explanatory Methods for Extracting Statistical Information from Complex Data, Springer Verlag, Berlin-Heidelberg 2000.
  • Borg I., Groenen P.J.F., Modern Multidimensional Scaling. Theory and Applications.
  • Second Edition, Springer-Verlag, New York 2005.
  • Busing F.M.T.A., Groenen P.J.K., Heiser W.J., Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation, ”Psychometrika” 2005, no. 1.
  • Davison M.L., Multidimensional Scaling, John Wiley and Sons, New York 1983.
  • Diday E., Noirhomme-Fraiture M., Symbolic Data Analysis and the SODAS Software, Wiley, Chichester 2008.
  • Dudek A., Metody analizy danych symbolicznych w badaniach ekonomicznych, Wydawnictwo UE, Wrocław 2013.
  • Groenen P.J.F., Winsberg S., Rodriguez O., Diday E., I-Scal: Multidimensional scaling of interval dissimilarities, “Computational Statistics and Data Analysis” 2006, vol. 51.
  • Groenen P.J.F., Winsberg S., Rodriguez O., Diday E., Multidimensional Scaling of Interval Dissi-milarities, Econometric Report, 2005-15, Erasmus University, Rotterdam 2005.
  • Lattin J., Carroll J.D., Green P.E., Analyzing Multivariate Data, Thomson Learning, Toronto 2003.
  • Lechevallier Y. (ed.), Scientific report for unsupervised classification, validation and cluster repre-sentation, Analysis System of Symbolic Official Data − Project number IST-2000-25161, Project Report, 2001.
  • Noirhomme-Fraiture M., Brito P., Far beyond the classical data models: Symbolic data analysis, “Statistical Analysis and Data Mining” 2011, vol. 4, issue 2, pp. 157-170.
  • Zaborski A., Zastosowanie algorytmu SMACOF do badań opartych na prostokątnej macierzy preferencji, [w:] Taksonomia 18, Klasyfikacja i analiza danych – teoria i zastosowania, K. Jajuga,M. Walesiak (red.), Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu nr 176, Wrocław 2011, pp. 262-271.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-4b6af648-2fd6-43ed-8c7f-06316d24d73e
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