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Improved Sammon Mapping Method for Visualization of Multidimensional Data

  • Halina Kwasnicka
  • Pawel Siemionko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7654)

Abstract

Three improvements to the Sammon mapping method are proposed. Two of them concern calculation complexity reduction. Introducing the limit for delta parameter allows to eliminate error fluctuations during data projection. Calculating distances not for all data points but for the part of them results in important reduction of the calculation time without worsening the final results. The third improvement allows adding new data to the projected ones without recalculation of all data from the beginning. The paper presents details of the proposed improvements and the performed experimental study.

Keywords

Data visualization Dimension reduction 

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References

  1. 1.
    Card, S., Mackinlay, J., Shneiderman, B.: Readings in Information Visualization - Using Vision to Think. Morgan Kaufmann (1999)Google Scholar
  2. 2.
    Keller, P.R., Keller, M.M.: Visual Cues. IEEE Press, Los Alamitos (1993)Google Scholar
  3. 3.
    Becker, B.G.: Volume rendering for relational data. In: IEEE Symposium on Information Visualization (InfoVis 1997), pp. 87–91 (1997)Google Scholar
  4. 4.
    Chambers, J., Cleveland, W., Kleiner, B., Tukey, P.: Graphical Methods for Data Analysis. Wadsworth (1983)Google Scholar
  5. 5.
    Inselberg, A.: The Plane with Parallel Coordinates. Special Issue on Computational Geometry: The Visual Computer 1, 69–91 (1985)zbMATHGoogle Scholar
  6. 6.
    Alley, T.R.: Physionomy and Social Perception. In: Social and Applied Aspects of Perceiving Faces, pp. 167–185 (1988)Google Scholar
  7. 7.
    Gibson, J.J.: The perception of the visual world. Houghton Mifflin Co., Boston (1950)Google Scholar
  8. 8.
    Pickett, R.M.: Response latency in a pattern perception situation. Acta Psychologica (27), 160–169 (1967)Google Scholar
  9. 9.
    Miller, G.: The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 276–291 (1956)CrossRefGoogle Scholar
  10. 10.
    Agrafiotis, D.K., Rassokhin, D.N., Lobanov, V.S.: Multidimensional scaling and visualization of large molecular similarity tables. Journal of Computational Chemistry 5(22), 488–500 (2001)Google Scholar
  11. 11.
    Lahdesmaki, H., Yli-Harja, O., Shmulevich, I., Zhang, W.: Distinguishing key biological pathways by knowledge based multidimensional scaling analysis: application to discriminate between primary breast cancers and their lymph node metastases. In: Yli-Harja, O., Shmulevich, I., Aho, T. (eds.) Proc. of the TICSP Workshop in Computational Systems Biology, WCSB 2003, Finland, vol. (21) (2003)Google Scholar
  12. 12.
    Sammon, J.W.J.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 401–409 (1969)Google Scholar
  13. 13.
    Karbauskaite, R., Dzemuda, G.: Multidimensional data projection algorithms saving calculations of distances. 123X Information Technology and Control (35) (2006)Google Scholar
  14. 14.
    Newman, A.A.: UCI Machine Learning Repository. University of California (2007), http://archive.ics.uci.edu/ml/index.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Halina Kwasnicka
    • 1
  • Pawel Siemionko
    • 1
  1. 1.Institute of InformaticsWroclaw University of TechnologyWroclawPoland

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