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Mathematical Visualization
Mathematical Visualization is a young new discipline. It offers efficient visualization tools to the classical subjects of mathematics, and applies mathematical techniques to problems in computer graphics and scientific visualization. Originally, it started in the interdisciplinary area of differential geometry, numerical mathematics, and computer graphics. In recent years, the methods developed have found important applications. The current volume is the quintessence of an international workshop in September 1997 in Berlin, focusing on recent developments in this emerging area. Experts present selected research work on new algorithms for visualization problems, describe the application and experiments in geometry, and develop new numerical or computer graphical techniques.
Parallel Coordinates
This is one book that can genuinely be said to be straight from the horse’s mouth. Written by the originator of the technique, it examines parallel coordinates as the leading methodology for multidimensional visualization. Starting from geometric foundations, this is the first systematic and rigorous exposition of the methodology's mathematical and algorithmic components. It covers, among many others, the visualization of multidimensional lines, minimum distances, planes, hyperplanes, and clusters of "near" planes. The last chapter explains in a non-technical way the methodology's application to visual and automatic data mining. The principles of the latter, along with guidelines, strategies and algorithms are illustrated in detail on real high-dimensional datasets.
Intelligent Computer Graphics 2009
The purpose of this volume is to present current work of the Intelligent Computer Graphics community, a community growing up year after year. This volume is a kind of continuation of the previously published Springer volume "Artificial Intelligence Techniques for Computer Graphics". Nowadays, intelligent techniques are more and more used in Computer Graphics in order, not only to optimise the processing time, but also to find more accurate solutions for a lot of Computer Graphics problems, than with traditional methods. This volume contains both invited and selected extended papers from the last 3IA Conference (3IA’2009), which has been held in Athens (Greece) in May 2009. The Computer Graphics areas approached in this volume are behavioural modelling, declarative modelling, intelligent modelling and rendering, data visualisation, scene understanding, realistic rendering, and more.
Scientific Visualization: The Visual Extraction of Knowledge from Data
One of the greatest scientific challenges of the 21st century is how to master, organize and extract useful knowledge from the overwhelming flow of information made available by today’s data acquisition systems and computing resources. Visualization is the premium means of taking up this challenge. This book is based on selected lectures given by leading experts in scientific visualization during a workshop held at Schloss Dagstuhl, Germany. Topics include user issues in visualization, large data visualization, unstructured mesh processing for visualization, volumetric visualization, flow visualization, medical visualization and visualization systems. The book contains more than 350 color illustrations.
Visualization Analysis and Design
Learn How to Design Effective Visualization SystemsVisualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques
Biomedical Informatics
Medical Informatics is defined as an interdisciplinary field studying the effective use of biomedical data, information and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health. To emphasize the broad character it is called Biomedical Informatics. The course LV 444.152 consists of the following 12 lectures: 1. Introduction: Computer Science meets Life Sciences, challenges and future directions; 2. Back to the future: Fundamentals of Data, Information and Knowledge; 3. Structured Data: Coding, Classification (ICD, SNOMED, MeSH, UMLS); 4. Biomedical Databases: Acquisition, Storage, Information Retrieval and Use; 5. Semi structured and weakly structured data; 6. Multimedia Data Mining and Knowledge Discovery; 7. Knowledge and Decision: Cognitive Science and Human-Computer Interaction; 8. Biomedical Decision Making: Reasoning and Decision Support; 9. Intelligent Information Visualization and Visual Analytics; 10. Biomedical Information Systems and Medical Knowledge Management; 11. Biomedical Data: Privacy, Safety and Security 12. Methodology for Information Systems: System Design, Usability and Evaluation
New Techniques and Technologies for Statistics II
The compilation and deployment of statistical techniques is nowadays almost universally based on computing systems. Rapidly changing technology is expanding the options available for improving the quality, range and delivery of statistics whilst reducing the cost, and at the same time is putting pressure on producers and users to keep up with the latest techniques, both as management views develop of what is possible and simply through peer group pressure. In the areas of official statistics, it is clear that new technologies will change our approach to the whole range to activities from systems design, through data collection, processing, analysis and dissemination, to the structure of the ...
Successes and New Directions in Data Mining
The problem of mining patterns is becoming a very active research area and efficient techniques have been widely applied to problems in industry, government, and science. From the initial definition and motivated by real-applications, the problem of mining patterns not only addresses the finding of itemsets but also more and more complex patterns. Successes and New Directions in Data Mining addresses existing solutions for data mining, with particular emphasis on potential real-world applications. Capturing defining research on topics such as fuzzy set theory, clustering algorithms, semi-supervised clustering, modeling and managing data mining patterns, and sequence motif mining, this book is an indispensable resource for library collections.
