http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
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.
by Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas.
Artificial Intelligence (incl. Robotics)
Springer Optimization and Its Applications, 75