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Mathematics | Multidimensional Data Visualization - Methods and Applications

Multidimensional Data Visualization

Methods and Applications

Dzemyda, Gintautas, Kurasova, Olga, Žilinskas, Julius

2012, XII, 252 p.

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  • • Presents an overview of multidimensional data visualization
  • • Provides backgroud to construction, analysis, and implementation of optimization algorithms for visualization of multidimensional data
  • Shows benefits of artificial neural networks and their integrated use with other methods for visualization of multidimensional data
  • Presents various applications of multidimensional data visualization:  from social sciences to medicine

The goal of this book is to present a variety of methods used  in multidimensional data visualization. The emphasis is placed on new research results and trends in this field, including optimization, artificial neural networks, combinations of algorithms, parallel computing, different proximity measures, nonlinear manifold learning,  and more. Many of the applications presented allow us to discover the obvious advantages of visual data mining—it is much easier for a decision maker to detect or extract useful information from graphical representation of data than from raw numbers.

The fundamental idea of visualization is to provide data in some visual form that lets humans  understand them, gain insight into the data, draw conclusions, and directly influence the process of decision making. Visual data mining is a field where human participation is integrated in the data analysis process; it covers data visualization and graphical presentation of information.

Multidimensional Data Visualization is intended for scientists and researchers in any field of study where complex and multidimensional data must be visually represented. It may also serve as a useful research supplement for PhD students in operations research, computer science, various fields of engineering,  as well as natural and social sciences.

Content Level » Research

Keywords » Zilinskas

Related subjects » Artificial Intelligence - Computational Science & Engineering - Mathematics - Theoretical Computer Science

Table of contents 

- Introduction.- Strategies for multidimensional data visualization.- Optimization-based visualization.- Artificial neural networks for visualization of multidimensional data.- Applications of visual multidimensional data analysis.- Index.

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