Multidimensional Data Visualization: Methods and Applications (Springer Optimization and Its Applications, nr. 75)

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This book highlights recent developments in multidimensional data visualization, presenting both new methods and modifications on classic techniques. Throughout the book, various  applications of multidimensional data visualization are presented including its uses in social sciences (economy, education, politics, psychology), environmetrics, and medicine (ophthalmology, sport medicine, pharmacology, sleep medicine).
The book provides recent research results in optimization-based visualization. Evolutionary algorithms and a two-level optimization method, based on combinatorial optimization and quadratic programming, are analyzed in detail. The performance of these algorithms and the development of parallel versions are discussed.
The utilization of new visualization techniques to improve the capabilies of artificial neural networks (self-organizing maps, feed-forward networks) is also discussed.
The book includes over 100 detailed images presenting examples of the many different visualization techniques that the book presents.
This book is intended for scientists and researchers in any field of study where complex and multidimensional data must be represented visually.
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ISBN-13: 9781489990006
ISBN-10: 1489990003
Pagini: 264
Dimensiuni: 155 x 235 x 14 mm
Greutate: 4.04 kg
Ediția: 2013
Editura: Springer
Colecția Springer
Seria Springer Optimization and Its Applications

Locul publicării: New York, NY, United States

Public țintă



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

Textul de pe ultima copertă

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.


• 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