Skip to main content
Book cover

Multidimensional Data Visualization

Methods and Applications

  • Book
  • © 2013

Overview

  • 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

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 75)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (5 chapters)

Keywords

About this book

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.

Authors and Affiliations

  • Institute of Mathematics & Informatics, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda, Olga Kurasova

  • Recognition Processes Department, Vilnius University, Vilnius, Lithuania

    Julius Žilinskas

Bibliographic Information

Publish with us