Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a "need-to-have" tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a "need to use, need to keep" resource following one's exploration of the subject.

This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced second edition has been fully revised and expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs. Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.

This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, held in Yangzhou, China, in October 2016. The 68 full papers presented were carefully reviewed and selected from 115 submissions. They provide a valuable and timely sample of latest research outcomes in data engineering and automated learning ranging from methodologies, frameworks, and techniques to applications including various topics such as evolutionary algorithms; deep learning; neural networks; probabilistic modeling; particle swarm intelligence; big data analysis; applications in regression, classification, clustering, medical and biological modeling and predication; text processing and image analysis.

This book constitutes the refereed proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013, held in Hefei, China, in October 2013. The 76 revised full papers presented were carefully reviewed and selected from more than 130 submissions. These papers provided a valuable collection of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, neural networks, probabilistic modelling, swarm intelligent, multi-objective optimisation, and practical applications in regression, classification, clustering, biological data processing, text processing, video analysis, including a number of special sessions on emerging topics such as adaptation and learning multi-agent systems, big data, swarm intelligence and data mining, and combining learning and optimisation in intelligent data engineering.

This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.

This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Analysis, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 88 submissions. All current aspects of intelligent data analysis are addressed, including intelligent support for modeling and analyzing data from complex, dynamical systems. The papers focus on novel applications of IDA techniques to, e.g., networked digital information systems; novel modes of data acquisition and the associated issues; robustness and scalability issues of intelligent data analysis techniques; and visualization and dissemination results.

Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now - at least in principle - solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of "drowning in information, but starving for knowledge" the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one's exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Hoeppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.

This book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphasis is placed on topics of importance for industrial applications, including self-tuning of fuzzy controllers, optimisation and stability analysis. The text begins with a detailed introduction to fuzzy systems and control theory, and guides the reader to a thorough understanding of up-to-date research results.

This book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphasis is placed on topics of importance for industrial applications, including self-tuning of fuzzy controllers, optimisation and stability analysis. The text begins with a detailed introduction to fuzzy systems and control theory, and guides the reader to a thorough understanding of up-to-date research results.

The series of Online World Conferences on Soft Computing (WSC) is organized by the World Federation of Soft Computing (WFSC) and has become an established annual event in the academic calendar and was already held for the 8th time in 2003. Starting as a small workshop held at Nagoya University, Japan in 1994 it has - tured to the premier online event on soft computing in industrial applications. It has been hosted by the universities of Granada, Spain, Fraunhofer Gesellschaft, Berlin, Cran?eld University, Helsinki University of Technology and Nagoya University. The goal of WFSC is to promote soft computing across the world, by using the internet as a forum for virtual technical discussion and publishing at no cost to authors and participants. The of?cial journal of the World Federation on Soft Computing is the journal Applied Soft Computing. The 8th WSC Conference (WSC8) took place from September 29th to October 10th, 2003. Registered participants had the opportunity to follow and discuss the online presentations of authors from all over the world. Out of more than 60 subm- sions the program committee had accepted 27 papers for ?nal presentation at WSC8.

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a "need-to-have" tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a "need to use, need to keep" resource following one's exploration of the subject.

Computergrafik umfasst die Erzeugung und Darstellung von einfachen Grafikelementen und Bildern bis hin zur Virtual Reality. Die Anwendung dieser Techniken profitiert von einem soliden Verstandnis der entsprechenden Grundlagen. Das erfolgreiche Buch von Prof. Klawonn, das jetzt bereits in der dritten Auflage vorliegt, vermittelt genau das - verstandlich und nachvollziehbar. Prof. Klawonn erlautert die wesentlichen Konzepte an konkreten Beispielen und bedient sich dabei der einfachen Sprachmittel der Javaprogrammierung. Die Umsetzung erfolgt praktisch mit Java 2D und Java 3D. Auch zur dritten Auflage gibt es wieder einen umfangreichen Online-Service mit Beispielprogrammen, Aufgaben und Loesungen, Folien und farbigen Illustrationen.

This book constitutes the refereed proceedings of the Second International Conference on Health Information Science, HIS 2013, held in London, UK, in March 2013. The 20 full papers presented together with 3 short papers, 3 demo papers and one poster in this volume were carefully reviewed and selected from numerous submissions. The papers cover all aspects of health information sciences and systems that support the health information management and health service delivery. The scope of the conference includes 1) medical/health/biomedicine information resources, such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyse, and optimize the use of information in the health domain, 2) data management, data mining, and knowledge discovery, all of which play a key role in the decision making, management of public health, examination of standards, privacy and security issues, and 3) development of new architectures and applications for health information systems.

Evolutionare Algorithmen bilden eine Klasse sehr universeller Werkzeuge zur Losung von Optimierungsproblemen. Mit diesem Buch lernen Sie alles Wesentliche uber dieses spannende Gebiet - ausgehend von den Grundlagen bis hin in die Anwendung. Es geht um Techniken wie genetische Algorithmen, Evolutionsstrategien und genetische Programmierung. Gewinnen Sie ein klares Verstandnis der zugrunde liegenden strategischen Arbeitsweise der einzelnen Algorithmen. Dies schafft die Voraussetzung fur den effizienten Einsatz der Optimierungsverfahren in der Praxis."

Eines der spannendsten Themen im Bereich intelligenter Systeme - von namhaften Autoren geschrieben - zum Lernen und Nachschlagen. Das Buch fuhrt in das Thema der Neuronalen Netze ein und weist daruber hinaus den Weg bis zum vollen Verstandnis modernster Fuzzy-Systeme. Neuronale Netze sind ein wichtiges Werkzeug in den Bereichen der Datenanalyse und Mustererkennung. Ursprunglich durch das biologische Vorbild inspiriert, wurde eine Vielfalt neuronaler Netze fur verschiedenste Anwendungen entwickelt. Ihre Kopplung mit Fuzzy-Systemen fuhrt zu den sogenannten Neuro-Fuzzy-Systemen. Diese weisen die Lernfahigkeit Neuronaler Netze auf und bieten gleichzeitig den Vorteil einer transparenten regelbasierten Struktur. Sie sind daher besonders vorteilhaft fur Anwendungsbereiche, in denen verstandliche Loesungen aus Daten erzeugt werden mussen.

Nachdem die ersten Fuzzy-Regler Anfang der siebziger Jahre entwickelt und in der Praxis erprobt wurden, hat das Gebiet der Fuzzy-Regelung in den vergangenen Jahrzehnten einen gewaltigen Fortschritt erfahren. Die zugrunde liegenden mathematischen und technischen Konzepte sind umfassend analysiert worden, und mittlerweile werden Fuzzy-Regler in vielen industriellen Anwendungen routinemassig eingesetzt. Das Ziel dieses Buches ist eine kritische Bestandsaufnahme der Fuzzy-Regler aus Sicht der klassischen Regelungstechnik. Der Schwerpunkt dieses Buches liegt in der Darstellung von Themen, die fur den Anwender von besonderem Interesse sind. Hierzu zahlen insbesondere die (Selbst-) Einstellung, Optimierung und Stabilitatsanalyse von Fuzzy-Reglern. Ausgehend von einer detaillierten Einfuhrung in die Gebiete Fuzzy-Systeme und Regelungstechnik wird der Leser systematisch an aktuelle Forschungsergebnisse herangefuhrt."

Dieses Buch ist das Standardwerk zu einem neuen Bereich der angewandten Fuzzy-Technologie, der Fuzzy-Clusteranalyse. Diese beinhaltet Verfahren der Mustererkennung zur Gruppierung und Strukturierung von Daten. Dabei werden im Gegensatz zu klassischen Clustering-Techniken die Daten nicht eindeutig zu Klassen zugeordnet, sondern Zugehorigkeitsgrade bestimmt, so dass die Fuzzy-Verfahren robust gegenuber gestorten oder verrauschten Daten sind und fliessende Klassenubergange handhaben konnen.
Dieses Werk gibt eine methodische Einfuhrung in die zahlreichen Fuzzy-Clustering-Algorithmen mit ihren Anwendungen in den Bereichen Datenanalyse, Erzeugung von Regeln fur Fuzzy-Regler, Klassifikations- und Approximationsprobleme sowie eine ausfuhrliche Darstellung des Shell-Clustering zur Erkennung von geometrischen Konturen in Bildern."

1 As a treatise on fuzzy set theory and its applications, "Foundations of Fuzzy Systems," by R. Kruse, J. Gebhardt and F. Klawonn, has few equals. Succinct, authoritative and up-to-date, it covers the basic theory very thoroughly and precisely, with emphasis on those aspects of the theory which play an important role in its applications. This is especially true of the chapters dealing with the calculus of fuzzy if-then rules - a subset of fuzzy set theory which plays a central role in the applications relating to the conception and design of both control and knowledge-based systems. To view the contents of "Foundations of Fuzzy Systems" in a proper perspective, a digression is in order. First, it is important to recognize that any crisp theory X can be fuzzified and hence generalized to fuzzy X - by replacing the concept of a crisp set in X by that of a fuzzy set. In application to basic fields such as arithmetic, topology, graph theory.

Die Autoren behandeln umfassend zentrale Themen der Informatik von Kunstlichen Neuronalen Netzen, uber Evolutionare Algorithmen bis hin zu Fuzzy-Systemen und Bayes-Netzen. Denn: Der Anwendungsbereich "Computational Intelligence" erlangt durch viele erfolgreiche industrielle Produkte immer mehr an Bedeutung. Dieses Buch behandelt die zentralen Techniken dieses Gebiets und bettet sie in ein didaktisches Konzept ein, welches sich gezielt an Studierende und Lehrende der Informatik wendet. Fur die vorliegende 2. Auflage des Buches wurden alle Themenbereiche uberarbeitet, aktualisiert und zum Teil erweitert. Zusatzmaterialen wie Aufgaben, Loesungen und Foliensatze fur Vorlesungen sowie Beispiele aus der industriellen Anwendung betonen den praktischen Charakter des Buches.

Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now - at least in principle - solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of "drowning in information, but starving for knowledge" the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one's exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Hoeppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.