Multidimensional Data Visualization
Subtitle: Methods and Applications
By: by Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas
Contributors: Kurasova, Olga; Žilinskas, Julius; SpringerLink (Online service)
Series: Springer Optimization and Its Applications -- 75
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] :
Number of pages: 250
ISBN-13: 9781441902368
Subjects: Visualization; Simulation and Modeling; Optimization; Computer simulation; Mathematics; Mathematical optimization; Artificial intelligence; Artificial Intelligence (incl. Robotics)
Description: <p>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.</p><p>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. </p><p><i>Multidimensional Data Visualization</i> 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.</p>
Table of contents: title: <p>Preface; title: 1. Multidimensional Data and the Concept of Visualization; title: 2. Strategies for Multidimensional Data Visualization; title: 3. Optimization-Based Visualization; title: 4. Combining Multidimensional Scaling with Artificial Neural Networks; title: 5. Applications of Visualizations; title: A. Test Data Sets; title: References; title: Index.</p>.