A Visual Analysis of Changes to Weighted Self-Organizing Map Patterns

  • Younjin ChungEmail author
  • Joachim Gudmundsson
  • Masahiro Takatsuka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)


Estimating output changes by input changes is the main task in causal analysis. In previous work, input and output Self-Organizing Maps (SOMs) were associated when conducting causal analysis of multivariate and nonlinear data. Based on the SOM association, a weight distribution of the output conditional on a given input was obtained over the output map space. Such a weighted SOM pattern of the output changes when the input changes. In order to analyze the pattern change, it is important to measure the difference of the patterns. Many methods have been proposed for measuring the dissimilarity of patterns; however, it is still a major challenge to identify how patterns are different. In this paper, we propose a visual approach for analyzing changes to weighted SOM patterns. This approach extracts features that represent the difference of patterns by change and facilitates overall and detailed comparisons of pattern changes. Ecological data are used to demonstrate the usefulness of our approach and the experimental results show that it visualizes the change information effectively.


Self-Organizing map Weighted SOM pattern Pattern dissimilarity Information visualization Pattern change analysis 



This research was partially supported by HMR+ SPARC Implementation Funding (BMRI 2015) under the Project G181478 and ARC’s Discovery Project funding scheme (DP150101134).


  1. 1.
    May, W.E.: Knowledge of causality in Hume and Aquinas. Thomist 34, 254–288 (1970)CrossRefGoogle Scholar
  2. 2.
    Chung, Y., Takatsuka, M.: A causal model using self-organizing maps. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9490, pp. 591–600. Springer, Cham (2015). doi: 10.1007/978-3-319-26535-3_67 CrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: Self-Organizing Maps. Information Sciences, 3rd edn. Springer, Heidelberg (2001). doi: 10.1007/978-3-642-56927-2 CrossRefzbMATHGoogle Scholar
  4. 4.
    Cha, S.H.: Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1, 300–307 (2007)Google Scholar
  5. 5.
    Kullback, S.: Information Theory and Statistics. Courier Corporation, North Chelmsford (1997)Google Scholar
  6. 6.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar
  7. 7.
    Niblack, C.W., Barber, R., Equitz, W., Flickner, M.D., Glasman, E.H., Petkovic, D., Yanker, P., Faloutsos, C., Taubin, G.: Qbic project: querying images by content, using color, texture, and shape. Proc. SPIE 1908, 173–187 (1993)CrossRefGoogle Scholar
  8. 8.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Technical report A57, Neural Networks Research Centre, Helsinki University of Technology (2000)Google Scholar
  9. 9.
    Vesanto, J.: SOM-based data visualization methods. Intell. Data Anal. 3(2), 111–126 (1999)CrossRefzbMATHGoogle Scholar
  10. 10.
    Ward, M.O.: Multivariate data glyphs: principles and practice. In: Handbook of Data Visualization. Springer Handbooks Computational Statistics, pp. 179–198. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-33037-0_8
  11. 11.
    Giddings, E.M.P., Bell, A.H., Beaulieu, K.M., Cuffney, T.F., Coles, J.F., Brown, L.R., Fitzpatrick, F.A., Falcone, J., Sprague, L.A., Bryant, W.L., Peppler, M.C., Stephens, C., McMahon, G.: Selected physical, chemical, and biological data used to study urbanizing streams in nine metropolitan areas of the United States, 1999–2004. Technical report Data Series 423, National Water-Quality Assessment Program, U.S. Geological Survey (2009)Google Scholar
  12. 12.
    Novotny, V., Virani, H., Manolakos, E.: Self organizing feature maps combined with ecological ordination techniques for effective watershed management. Technical Report 4, Center for Urban Environmental Studies, Northeastern University, Boston (2005)Google Scholar
  13. 13.
    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 (1997)Google Scholar
  14. 14.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2 edn. Cambridge University Press, Cambridge (1992)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Younjin Chung
    • 1
    Email author
  • Joachim Gudmundsson
    • 1
  • Masahiro Takatsuka
    • 1
  1. 1.School of IT, Faculty of Engineering and ITThe University of SydneyCamperdownAustralia

Personalised recommendations