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A Projection Pursuit Method on the multidimensional squared Contingency Table

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  • Ju Ahn
  • Heike Hofmann
  • Dianne Cook

Abstract

In this study a projection pursuit method is used to explore c d (square) contingency table data. The method operates on projection matrices constructed from the contingency tables using affine geometry and creates projections (or marginals) using a Radon transform. The projection matrices and the projections can be used to find the “interesting” (nonuniform structure), and to cluster and to order the, cases. This projection pursuit method is implemented with graph visualization of projection. It is similar to the discrete version of Andrews’ curve. We demonstrate how this approach compares to association rules commonly used in data mining using a market basket data set and compare the PP results with the analysis of a data set from Wishart and Leach) (1970). Copyright Physica-Verlag 2003

Suggested Citation

  • Ju Ahn & Heike Hofmann & Dianne Cook, 2003. "A Projection Pursuit Method on the multidimensional squared Contingency Table," Computational Statistics, Springer, vol. 18(3), pages 605-626, September.
    • Handle: RePEc:spr:compst:v:18:y:2003:i:3:p:605-626
    DOI: 10.1007/BF03354619
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    References listed on IDEAS

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    1. Polzehl, Jorg, 1995. "Projection pursuit discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 20(2), pages 141-157, August.
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    Cited by:

    1. Ursula Laa & Dianne Cook, 2020. "Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics," Computational Statistics, Springer, vol. 35(3), pages 1171-1205, September.

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