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Symbolic Multidimensional Scaling Versus Noisy Variables and Outliers

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Classification as a Tool for Research

Abstract

The aim of the paper is to present and compare effectiveness of symbolic multidimensional scaling methods when we are dealing data with noisy variables and/or outliers. In the article basic terms of symbolic data analysis and symbolic multidimensional scaling are presented. In empirical part simulation experiment results with application of Interscal and I-Scal (random and rational start point) are compared based on artificial data (containing noisy variables and/or outliers) generated by cluster.Gen procedure from clusterSim package of R software.

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References

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Author information

Authors and Affiliations

  1. Department of Econometrics and Computer Science, Wroclaw University of Economics, Wroclaw, Poland

    Marcin Pełka

Authors
  1. Marcin Pełka
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Correspondence to Marcin Pełka .

Editor information

Editors and Affiliations

  1. LS für BWL, insb. Finanzwirtschaft und, Finanzdienstleistungen, TU Dresden, Helmholtzstr. 10, Dresden, 01062, Germany

    Hermann Locarek-Junge

  2. FG Computergestützte Statistik, Univ. Dortmund, Vogelpothsweg 87, Dortmund, 44227, Germany

    Claus Weihs

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Pełka, M. (2010). Symbolic Multidimensional Scaling Versus Noisy Variables and Outliers. In: Locarek-Junge, H., Weihs, C. (eds) Classification as a Tool for Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10745-0_37

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