IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-1-4419-0236-8_5.html
   My bibliography  Save this book chapter

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Danutė Krapavickaitė, 2022. "Coherence Coefficient for Official Statistics," Mathematics, MDPI, vol. 10(7), pages 1-20, April.
    2. 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.
    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 & 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.
    5. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-1-4419-0236-8_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.