Applications of Visualization

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


This chapter is intended for applications of multidimensional data visualization. Some application examples and interpretations of the results are presented. These applications reveal the possibilities and advantages of the visual analysis. The applications can be grouped as follows: in social sciences, in medicine and pharmacology, and visual analysis of correlation matrices.


  1. 9.
    Bernatavičienė, J., Dzemyda, G., Kurasova, O., Marcinkevičius, V., Medvedev, V.: The problem of visual analysis of multidimensional medical data. In: Models and Algorithms for Global Optimization, vol. 4, pp. 277–298. Springer, New York (2007). DOI 10.1007/ 978-0-387-36721-7_17Google Scholar
  2. 13.
    Blanco, I.D., Vega, A.A.C., González, A.B.D.: Correlation visualization of high dimensional data using topographic maps. In: ICANN’02: Proceedings of the International Conference on Artificial Neural Networks, pp. 1005–1012. Springer, London (2002)Google Scholar
  3. 24.
    Buteikienė, D., Paunksnis, A., Barzdžiukas, V., Bernatavičienė, J., Marcinkevičius, V., Treigys, P.: Assessment of the optic nerve disc and excavation parameters of interactive and automated parameterization methods. Informatica 23(3), 335–356 (2012)MathSciNetGoogle Scholar
  4. 28.
    Chen, Z., Ivanov, P.C., Hu, K., Stanley, H.E.: Effect of nonstationarities on detrended fluctuation analysis. Phys. Rev. E 65(4), 041,107 (2002). DOI 10.1103/PhysRevE.65.041107Google Scholar
  5. 46.
    Dzemyda, G.: Clustering of parameters on the basis of correlations via simulated annealing. Contr. Cybern. Special Issue on Simulated Annealing Applied to Combinatorial Optimization 25(1), 55–74 (1996)zbMATHGoogle Scholar
  6. 47.
    Dzemyda, G.: Visualization of a set of parameters characterized by their correlation matrix. Comput. Stat. Data Anal. 36(1), 15–30 (2001). DOI 10.1016/S0167-9473(00)00030-XCrossRefzbMATHMathSciNetGoogle Scholar
  7. 48.
    Dzemyda, G.: Visualization of correlation-based environmental data. Environmetrics 15(8), 827–836 (2004). DOI 10.1002/env.672CrossRefGoogle Scholar
  8. 49.
    Dzemyda, G.: Multidimensional data visualization in the statistical analysis of curricula. Comput. Stat. Data Anal. 49(1), 265–281 (2005). DOI 10.1016/j.csda.2004.05.001CrossRefzbMATHMathSciNetGoogle Scholar
  9. 54.
    Dzemyda, G., Kurasova, O.: Dimensionality problem in the visualization of correlation-based data. In: ICANNGA’07: Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, Part II. Lecture Notes in Computer Science, pp. 544–553. Springer, Berlin (2007). DOI  10.1007/978-3-540-71629-7_61
  10. 56.
    Dzemyda, G., Tiešis, V.: Visualization of multidimensional objects and the socio-economical impact to activity in EC RTD databases. Informatica 12(2), 239–262 (2001)zbMATHGoogle Scholar
  11. 57.
    Dzemyda, G., Šaltenis, V., Tiešis, V.: Forecasting models in the state education system. Informat. Educ. 2(1), 3–14 (2003)Google Scholar
  12. 61.
    Fautin, D., Buddemeier, R.: Biogeoinformatics of hexacorallia (corals, sea anemones, and their allies): Interfacing geospatial, taxonomic, and environmental data for a group of marine invertebrates (2001). URL Scholar
  13. 85.
    Harman, H.H.: Modern Factor Analysis, 3 edn. University Of Chicago Press, Chicago (1976)Google Scholar
  14. 91.
    Hellemaa, P.: The development of coastal dunes and their vegetation in finland. Ph.D. thesis, University of Helsinki, Department of Geography (1998)Google Scholar
  15. 101.
    Hwa, J., Graham, R.M., Perez, D.M.: Identification of critical determinants of α1-adrenergic receptor subtype selective agonist binding. J. Biol. Chem. 270(39), 23189–23195 (1995). DOI 10.1074/jbc.270.39.23189CrossRefGoogle Scholar
  16. 102.
    Ieno, E.: Las comunidades bentonicas de fondos blandos del norte de la provincia de buenos aires: Su rol ecologico en el ecosistema costero. Ph.D. thesis, Universidad Nacional de Mar del Plata (2000). URL
  17. 110.
    Jolliffe, I.: Principal Component Analysis. Springer, Berlin (1986)CrossRefGoogle Scholar
  18. 170.
    Paunksnis, A., Barzdžiukas, V., Jegelevičius, D., Kurapkienė, S., Dzemyda, G.: The use of information technologies for diagnosis in ophthalmology. J. Telemed. Telecare 12, 37–40 (2006). DOI 10.1258/ 135763306777978443CrossRefGoogle Scholar
  19. 172.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88(6), 2297–2301 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 186.
    Ruuskanen, J.O., Laurila, J., Xhaard, H., Rantanen, V.V., Vuoriluoto, K., Wurster, S., Marjamäki, A., Vainio, M., Johnson, M.S., Scheinin, M.: Conserved structural, pharmacological and functional properties among the three human and five zebrafish α2-adrenoceptors. Br. J. Pharmacol. 144(2), 165–177 (2005). DOI 10.1038/sj.bjp.0706057CrossRefGoogle Scholar
  21. 196.
    Šaltenis, V., Dzemyda, G., Tiešis, V.: Quantitative forecasting and assessment models in the state education system. Informatica 13(4), 485–500 (2002)Google Scholar
  22. 199.
    Telser, S., Staudacher, M., Ploner, Y., Amann, A., Hinterhuber, H., Ritsch-Marte, M.: Can one detect sleep stage transitions for on-line sleep scoring by monitoring the heart rate variability? Somnologie Schlafforschung und Schlafmedizin 8, 33–41 (2004). DOI 10.1111/j. 1439-054X.2004.00016.xCrossRefGoogle Scholar
  23. 204.
    Treigys, P., Dzemyda, G., Barzdžiukas, V.: Automated positioning of overlapping eye fundus images. In: Proceedings of the 8th International Conference on Computational Science, Part I, ICCS’08, pp. 770–779. Springer, Berlin (2008). DOI  10.1007/978-3-540-69384-0_82
  24. 205.
    Treigys, P., Šaltenis, V., Dzemyda, G., Barzdžiukas, V., Paunksnis, A.: Automated optic nerve disc parameterization. Informatica 19(3), 403–420 (2008)Google Scholar
  25. 207.
    Uhlén, S., Dambrova, M., Näsman, J., Schiöth, H.B., Gu, Y., Wikberg-Matsson, A., Wikberg, J.E.S.: [3h]rs79948-197 binding to human, rat, guinea pig and pig α2A-, α2B- and α2C-adrenoceptors. comparison with mk912, rx821002, rauwolscine and yohimbine. Eur. J. Pharmacol. 343(1), 93–101 (1998). DOI 10.1016/S0014-2999(97) 01521-5Google Scholar
  26. 222.
    Žičkus, M.: Influence of meteorological parameters on the urban air pollution and its forecast. Ph.D. thesis, Vilnius University (1998)Google Scholar
  27. 232.
    Žilinskas, J.: Multidimensional scaling in protein and pharmacological sciences. In: Bogle, I.D.L., Žilinskas, J. (eds.) Computer Aided Methods in Optimal Design and Operations, Series on Computers and Operations Research, vol. 7, pp. 139–148. World Scientific, Singapore (2006). DOI 10.1142/9789812772954_0015Google 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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