Identification of Noisy Variables for Nonmetric and Symbolic Data in Cluster Analysis

  • Marek Walesiak
  • Andrzej Dudek
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


A proposal of an extended version of the HINoV method for the identification of the noisy variables (Carmone et al. (1999)) for nonmetric, mixed, and symbolic interval data is presented in this paper. Proposed modifications are evaluated on simulated data from a variety of models. The models contain the known structure of clusters. In addition, the models contain a different number of noisy (irrelevant) variables added to obscure the underlying structure to be recovered.


Cluster Structure Ordinal Data Symbolic Data Multivariate Normal Distribution Rand Index 
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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marek Walesiak
    • 1
  • Andrzej Dudek
    • 1
  1. 1.Department of Econometrics and Computer ScienceWroclaw University of EconomicsJelenia GoraPoland

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