Data Analytics Models and Algorithms for Intelligent Data Analysis
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analy
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Data Analytics Models and Algorithms for Intelligent Data Analysis 2nd Edition
Data Analytics
Thomas A. Runkler
Data Analytics Models and Algorithms for Intelligent Data Analysis 2nd Edition
Thomas A. Runkler Siemens AG München, Germany
ISBN: 978-3-658-14074-8 DOI 10.1007/978-3-658-14075-5
ISBN: 978-3-658-14075-5 (eBook)
Library of Congress Control Number: 2016942272 Springer Vieweg © Springer Fachmedien Wiesbaden 2012, 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer Vieweg is a brand of Springer Nature The registered company is Springer Fachmedien Wiesbaden GmbH
Preface
The information in the world doubles every 20 months. Important data sources are business and industrial processes, text and structured databases, images and videos, and physical and biomedical data. Data analytics allows to find relevant information, structures, and patterns, to gain new insights, to identify causes and effects, to predict future developments, or to suggest optimal decisions. We need models and algorithms to collect, preprocess, analyze, and evaluate data, from various fields such as statistics, machine learning, pattern recognition, system theory, operations research, or artificial intelligence. With this book, you will learn about the most important methods and algorithms for data analytics. You will be able to choose appropriate methods for specific tasks and apply these in your own data analytics projects. You will understand the basic concepts of the growing field of data analytics, which will allow you to keep pace and to actively contribute to the advancement of the field. This text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. The book is structured according to typical practical data analytics projects. Only basic mathematics is required. This material has been used for more than
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