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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.
List of contents
Preface.- 1. Multidimensional Data and the Concept of Visualization.- 2. Strategies for Multidimensional Data Visualization.- 3. Optimization-Based Visualization.- 4. Combining Multidimensional Scaling with Artificial Neural Networks.- 5. Applications of Visualizations.- A. Test Data Sets.- References.- Index.
Summary
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.
