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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.

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