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Applications of Visualization

In: Multidimensional Data Visualization

Author

Listed:
  • Gintautas Dzemyda

    (Vilnius University)

  • Olga Kurasova

    (Vilnius University)

  • Julius Žilinskas

    (Vilnius University)

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.

Suggested Citation

  • Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Applications of Visualization," Springer Optimization and Its Applications, in: Multidimensional Data Visualization, edition 127, chapter 0, pages 179-226, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-0236-8_5
    DOI: 10.1007/978-1-4419-0236-8_5
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    Citations

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    Cited by:

    1. Danutė Krapavickaitė, 2022. "Coherence Coefficient for Official Statistics," Mathematics, MDPI, vol. 10(7), pages 1-20, April.
    2. Jurgita Markevičiūtė & Jolita Bernatavičienė & Rūta Levulienė & Viktor Medvedev & Povilas Treigys & Julius Venskus, 2022. "Impact of COVID-19-Related Lockdown Measures on Economic and Social Outcomes in Lithuania," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    3. Dzemyda, Gintautas & Sabaliauskas, Martynas, 2021. "Geometric multidimensional scaling: A new approach for data dimensionality reduction," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    4. Arturas Kaklauskas & Edmundas Kazimieras Zavadskas & Bjoern Schuller & Natalija Lepkova & Gintautas Dzemyda & Jurate Sliogeriene & Olga Kurasova, 2020. "Customized ViNeRS Method for Video Neuro-Advertising of Green Housing," IJERPH, MDPI, vol. 17(7), pages 1-28, March.
    5. Arturas Kaklauskas & Gintautas Dzemyda & Laura Tupenaite & Ihar Voitau & Olga Kurasova & Jurga Naimaviciene & Yauheni Rassokha & Loreta Kanapeckiene, 2018. "Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment," Energies, MDPI, vol. 11(8), pages 1-20, August.

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