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Visualizing Latent Structures in Grade Correspondence Cluster Analysis and Generalized Association Plots

  • Wieslaw Szczesny
  • Marek Wiech
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

The latent structure of psychological data set concerning superstitions is investigated by means of two recent exploratory methods: Grade Correspondence Cluster Analysis (GCCA) and Generalized Association Plots (GAP). The paper compares visualized results in GCCA and GAP. Moreover, it shows what differs both methodologies and what is their intrinsic similarity, according to which the revealed latent structures become equivalent whenever the data set is sufficiently regular. Therefore upon the basis of the real data set, were constructed two types of highly regular simulated data, of the same size and the same multivariate dependence index. These simulated data were then analyzed.

Keywords

Latent Structure Correlation Matrice Positive Dependence Pearson Correlation Matrix Uniform Discretization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2006

Authors and Affiliations

  • Wieslaw Szczesny
    • 1
  • Marek Wiech
    • 2
  1. 1.Institute of Computer SciencePolish Academy of SciencesPoland
  2. 2.Department of Econometrics and InformaticsWarsaw Agricultural UniversityWarsaw

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