Multidimensional Data and the Concept of Visualization

  • Gintautas Dzemyda
  • Olga Kurasova
  • Julius Žilinskas
Part of the Springer Optimization and Its Applications book series (SOIA, volume 75)


It is often desirable to visualize a data set, the items of which are described by more than three features. Therefore, we have multidimensional data, and our goal is to make some visual insight into the data set analyzed. For human perception, the data must be represented in a low-dimensional space, usually of two or three dimensions. The goal of visualization methods is to represent the multidimensional data in a low-dimensional space so that certain properties (e.g. clusters, outliers) of the structure of the data set were preserved as faithfully as possible. Such a visualization of data is highly important in data mining because recent applications produce a large amount of data that require specific means for knowledge discovery. The dimensionality reduction or visualization methods are recent techniques to discover knowledge hidden in multidimensional data sets.


Dimensionality Reduction Linear Discriminant Analysis Visualization Method Multidimensional Data Nonlinear Projection 
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.


  1. 17.
    Brunsdon, C., Fotheringham, A., Charlton, M.: An investigation of methods for visualising highly multivariate datasets. In: Unwin, D., Fisher, P. (eds.) Case Studies of Visualization in the Social Sciences, Joint Information Systems Committee, ESRC, Technical Report Series 43, pp. 55–80. Birkbeck College (1998)Google Scholar
  2. 27.
    Chen, C.H., Hrdle, W., Unwin, A.: Handbook of Data Visualization (Springer Handbooks of Computational Statistics). Springer, New York (2008)Google Scholar
  3. 55.
    Dzemyda, G., Kurasova, O., Medvedev, V.: Dimension reduction and data visualization using neural networks. In: Maglogiannis, I., Karpouzis, K., Wallace, M., Soldatos, J. (eds.) Emerging Artificial Intelligence Applications in Computer Engineering, Frontiers in Artificial Intelligence and Applications, vol. 160, pp. 25–49. IOS, Amsterdam (2007)Google Scholar
  4. 73.
    Grinstein, G., Trutschl, M., Cvek, U.: High-dimensional visualizations. In: Proceedings of Workshop on Visual Data Mining, ACM Conference on Knowledge Discovery and Data Mining, pp. 1–14. ACM, New York (2001)Google Scholar
  5. 74.
    Grinstein, G.G., Ward, M.O.: Introduction to data visualization. In: Fayyad, U., Grinstein, G.G., Wierse, A. (eds.) Information visualization in data mining and knowledge discovery, pp. 21–45. Morgan Kaufmann, San Francisco, CA (2002)Google Scholar
  6. 94.
    Hoffman, P.E., Grinstein, G.G.: A survey of visualizations for high-dimensional data mining. In: Fayyad, U., Grinstein, G.G., Wierse, A. (eds.) Information Visualization in Data Mining and Knowledge Discovery, pp. 47–82. Morgan Kaufmann, San Francisco, CA (2002)Google Scholar
  7. 109.
    John, A.L., Verleysen, M.: Nonlinear Dimensionality Reduction. Information Science and Statistics. Springer, New York (2007)MATHGoogle Scholar
  8. 117.
    Keim, D.A., Kriege, H.P.: Visualization techniques for mining large databases: A comparison. IEEE Trans. Knowl. Data Eng. 8, 923–938 (1996)CrossRefGoogle Scholar
  9. 118.
    Keim, D.A., Ward, M.: Visualization. In: Intelligent Data Analysis: An Introduction, pp. 403–427. Springer, New York (2003)Google Scholar
  10. 187.
    Sachinopoulou, A.: Multidimensional visualization. Tech. rep., Technical Research Centre of Finland, VTT Tiedotteita, Meddelanden, Research Notes 2114 (2001)Google Scholar
  11. 195.
    Šaltenis, V., Aušraitė, J.: Data visualization: ideas, methods, and problems. Informat. Educ. 1(1), 129–148 (2002)Google Scholar
  12. 217.
    Wong, P.C., Bergeron, R.D.: 30 years of multidimensional multivariate visualization. In: Scientific Visualization, Overviews, Methodologies, and Techniques, pp. 3–33. IEEE Computer Society, Washington, DC (1997)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Gintautas Dzemyda
    • 1
  • Olga Kurasova
    • 1
  • Julius Žilinskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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