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Applications of Visualization

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Multidimensional Data Visualization

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 75))

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Abstract

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.

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References

  1. 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_17

    Google Scholar 

  2. 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. 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)

    MathSciNet  Google Scholar 

  4. 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.65.041107

    Google Scholar 

  5. 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)

    MATH  Google Scholar 

  6. 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-X

    Article  MATH  MathSciNet  Google Scholar 

  7. Dzemyda, G.: Visualization of correlation-based environmental data. Environmetrics 15(8), 827–836 (2004). DOI 10.1002/env.672

    Article  Google Scholar 

  8. 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.2004.05.001

    Article  MATH  MathSciNet  Google Scholar 

  9. 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. 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)

    MATH  Google Scholar 

  11. Dzemyda, G., Šaltenis, V., Tiešis, V.: Forecasting models in the state education system. Informat. Educ. 2(1), 3–14 (2003)

    Google Scholar 

  12. 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 http://www.kgs.ku.edu/Hexacoral/Envirodata/Correlations/corre%l1.htm

    Google Scholar 

  13. Harman, H.H.: Modern Factor Analysis, 3 edn. University Of Chicago Press, Chicago (1976)

    Google Scholar 

  14. 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. 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.270.39.23189

    Article  Google Scholar 

  16. 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 http://www.brodgar.com/benthos.htm

  17. Jolliffe, I.: Principal Component Analysis. Springer, Berlin (1986)

    Book  Google Scholar 

  18. 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/ 135763306777978443

    Article  Google Scholar 

  19. Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88(6), 2297–2301 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  20. 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.0706057

    Article  Google Scholar 

  21. 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. 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.x

    Article  Google Scholar 

  23. 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. Treigys, P., Šaltenis, V., Dzemyda, G., Barzdžiukas, V., Paunksnis, A.: Automated optic nerve disc parameterization. Informatica 19(3), 403–420 (2008)

    Google Scholar 

  25. 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) 01521-5

    Google Scholar 

  26. Žičkus, M.: Influence of meteorological parameters on the urban air pollution and its forecast. Ph.D. thesis, Vilnius University (1998)

    Google Scholar 

  27. 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_0015

    Google Scholar 

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Dzemyda, G., Kurasova, O., Žilinskas, J. (2013). Applications of Visualization. In: Multidimensional Data Visualization. Springer Optimization and Its Applications, vol 75. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0236-8_5

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