Shadow Algorithms Data Miner provides a high-level understanding of the complete set of shadow concepts and algorithms, addressing their usefulness from a larger graphics system perspective. It discusses the applicability and limitations of all the direct illumination approaches for shadow generation.With an emphasis on shadow fundamentals, the boo

Thoroughly updated, this fourth edition focuses on modern techniques used to generate synthetic three-dimensional images in a fraction of a second. With the advent of programmable shaders, a wide variety of new algorithms have arisen and evolved over the past few years. This edition discusses current, practical rendering methods used in games and other applications. It also presents a solid theoretical framework and relevant mathematics for the field of interactive computer graphics, all in an approachable style. New to this edition: new chapter on VR and AR as well as expanded coverage of Visual Appearance, Advanced Shading, Global Illumination, and Curves and Curved Surfaces.

Der Prix Ars Electronica ist der traditionsreichste Medienkunstwettbewerb der Welt. Seit 1987 alljährlich ausgeschrieben, gilt er wegen seiner Kontinuität, der hohen Anzahl sowie Qualität der Einreichungen als Trendbarometer der weltweiten Medienkunst. Mit vielen Bildern, Texten und Statements der Jury bündelt das Buch jene Arbeiten, die 2020 in den Kategorien Computer Animation, Digital Musics & Sound Art, Artificial Intelligence & Life Art und u19 – Create Your World ausgezeichnet wurden. Ebenfalls im Buch enthalten ist wieder ein Best-of des im Auftrag der Europäischen Kommission ausgeschriebenen STARTS-Prize. Im Fokus dieses hoch dotierten Wettbewerbs standen innovative Projekte an der Schnittstelle von Wissenschaft, Technologie und Kunst (= Science, Technology and ARTS).

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

"All source codes, as well as data sets used in computational experiments, are included in the accompanying CD-ROM." -- p. [xv]

This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance.

A textbook for postgraduate students and industry professionals.

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.

Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware d