Multidimensional Data and the Concept of Visualization

  • Gintautas Dzemyda
  • Olga Kurasova
  • Julius Žilinskas
Part of the Springer Optimization and Its Applications book series (SOIA, volume 75)


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.


Dimensionality Reduction Linear Discriminant Analysis Visualization Method Multidimensional Data Nonlinear Projection 


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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Gintautas Dzemyda
    • 1
  • Olga Kurasova
    • 1
  • Julius Žilinskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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