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Data Visualization through Graph Drawing

Author

Listed:
  • George Michailidis

    (The University of Michigan)

  • Jan Leeuw

    (University of California, Los Angeles)

Abstract

Summary In this paper the problem of visualizing categorical multivariate data sets is considered. By representing the data as the adjacency matrix of an appropriately defined bipartite graph, the problem is transformed to one of graph drawing. A general graph drawing framework is introduced, the corresponding mathematical problem defined and an algorithmic approach for solving the necessary optimization problem discussed. The new approach is illustrated through several examples.

Suggested Citation

  • George Michailidis & Jan Leeuw, 2001. "Data Visualization through Graph Drawing," Computational Statistics, Springer, vol. 16(3), pages 435-450, September.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:3:d:10.1007_s001800100077
    DOI: 10.1007/s001800100077
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    References listed on IDEAS

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    1. Michailidis, George & de Leeuw, Jan, 2000. "Multilevel homogeneity analysis with differential weighting," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 411-442, January.
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    Cited by:

    1. Kumasaka, Natsuhiko & Shibata, Ritei, 2008. "High-dimensional data visualisation: The textile plot," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3616-3644, March.
    2. repec:jss:jstsof:31:i04 is not listed on IDEAS
    3. Zhao, S.L. & Cacciolatti, L. & Lee, S.H. & Song, W., 2015. "Regional collaborations and indigenous innovation capabilities in China: A multivariate method for the analysis of regional innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 202-220.
    4. Michailidis, George & De Leeuw, Jan, 2005. "Homogeneity analysis using absolute deviations," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 587-603, March.

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