This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. In addition to theory-oriented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicology describe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl), the German Association for Pattern Recognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011.​

This open access book begins with an algorithm–a set of IF...THEN rules used in the development of a new, ethical, video surveillance architecture for transport hubs. Readers are invited to follow the algorithm over three years, charting its everyday life. Questions of ethics, transparency, accountability and market value must be grasped by the algorithm in a series of ever more demanding forms of experimentation. Here the algorithm must prove its ability to get a grip on everyday life if it is to become an ordinary feature of the settings where it is being put to work. Through investigating the everyday life of the algorithm, the book opens a conversation with existing social science research that tends to focus on the power and opacity of algorithms. In this book we have unique access to the algorithm’s design, development and testing, but can also bear witness to its fragility and dependency on others.

Presenting a theory of the theoryless, a computer scientist provides a model of how effective behavior can be learned even in a world as complex as our own, shedding new light on human nature.

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.

To examine, analyze, and manipulate a problem to the point of designing an algorithm for solving it is an exercise of fundamental value in many fields. With so many everyday activities governed by algorithmic principles, the power, precision, reliability and speed of execution demanded by users have transformed the design and construction of algorithms from a creative, artisanal activity into a full-fledged science in its own right. This book is aimed at all those who exploit the results of this new science, as designers and as consumers. The first chapter is an overview of the related history, demonstrating the long development of ideas such as recursion and more recent formalizations such as computability. The second chapter shows how the design of algorithms requires appropriate techniques and sophisticated organization of data. In the subsequent chapters the contributing authors present examples from diverse areas – such as routing and networking problems, Web search, information security, auctions and games, complexity and randomness, and the life sciences – that show how algorithmic thinking offers practical solutions and also deepens domain knowledge. The contributing authors are top-class researchers with considerable academic and industrial experience; they are also excellent educators and communicators and they draw on this experience with enthusiasm and humor. This book is an excellent introduction to an intriguing domain and it will be enjoyed by undergraduate and postgraduate students in computer science, engineering, and mathematics, and more broadly by all those engaged with algorithmic thinking.

Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniqu

This book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage: - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations