IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-1-4419-0236-8_1.html
   My bibliography  Save this book chapter

Multidimensional Data and the Concept of Visualization

In: Multidimensional Data Visualization

Author

Listed:
  • Gintautas Dzemyda

    (Vilnius University)

  • Olga Kurasova

    (Vilnius University)

  • Julius Žilinskas

    (Vilnius University)

Abstract

It is often desirable to visualize a data set, the items of which are described by more than three features. Therefore, we have multidimensional data, and our goal is to make some visual insight into the data set analyzed. For human perception, the data must be represented in a low-dimensional space, usually of two or three dimensions. The goal of visualization methods is to represent the multidimensional data in a low-dimensional space so that certain properties (e.g. clusters, outliers) of the structure of the data set were preserved as faithfully as possible. Such a visualization of data is highly important in data mining because recent applications produce a large amount of data that require specific means for knowledge discovery. The dimensionality reduction or visualization methods are recent techniques to discover knowledge hidden in multidimensional data sets.

Suggested Citation

  • Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Multidimensional Data and the Concept of Visualization," Springer Optimization and Its Applications, in: Multidimensional Data Visualization, edition 127, chapter 0, pages 1-4, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-0236-8_1
    DOI: 10.1007/978-1-4419-0236-8_1
    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. Lorena Parra-Rodríguez & Edward Reyes-Ramírez & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet & Carmen García-Peña, 2022. "Self-Organizing Maps to Multidimensionally Characterize Physical Profiles in Older Adults," IJERPH, MDPI, vol. 19(19), pages 1-25, September.
    2. Deqiang Cheng & Chunliu Gao, 2022. "Regionalization Research of Mountain-Hazards Developing Environments for the Eurasian Continent," Land, MDPI, vol. 11(9), pages 1-19, September.

    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_1. 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.