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Guide to Intelligent Data Analysis

How to Intelligently Make Sense of Real Data

  • Textbook
  • © 2010

Overview

Authors:
  1. Michael R. Berthold

    1. FB Informatik und, Informationswissenschaft, Universität Konstanz, Konstanz, Germany

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  2. Christian Borgelt

    1. Intelligent Data Analysis & Graphical, Models Research Unit, European Centre for Soft Computing, Mieres, Spain

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  3. Frank Höppner

    1. FB Wirtschaft, Ostfalia University of Applied Sciences, Wolfsburg, Germany

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  4. Frank Klawonn

    1. FB Informatik, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

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  • Presents a broad-range of perspectives on data analysis, providing readers with a comprehensive account of the field
  • Focuses on the practical aspects as well as presenting the theory comprehensively
  • A special emphasis is given to put on pointing out the pitfalls that lead to wrong or insufficient analysis of results
  • Hands-on examples are given to provide readers with further insight into the topic
  • Includes supplementary material: sn.pub/extras

Part of the book series: Texts in Computer Science (TCS)

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Table of contents (10 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 1-14

  3. Practical Data Analysis: An Example

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 15-23

  4. Project Understanding

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 25-32

  5. Data Understanding

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 33-79

  6. Principles of Modeling

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 81-114

  7. Data Preparation

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 115-143

  8. Finding Patterns

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 145-206

  9. Finding Explanations

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 207-258

  10. Finding Predictors

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 259-296

  11. Evaluation and Deployment

    • Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
    Pages 297-301

  12. Back Matter

    Pages 303-394
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Keywords

About this book

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 examplesto 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 Höppner 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.

Reviews

From the reviews:

“The authors, leading scholars in the field based in Germany and Spain, seek to offer a hands-on instructional approach to basic data analysis techniques and consider their use in solving problems. The reader is taken through the process, following the interlinked steps of project understanding, data understanding, data preparation, modelling, and deployment and monitoring. The text reviews the basics of classical statistics that support and justify many data analysis methods, and includes a glossary of statistical terms.” (Times Higher Education, 26 May 2011)

“The clear and complete exposition of arguments, along with the attention to formalization and the balanced number of bibliographic references, make this book a bright introduction to intelligent data analysis. It is an excellent choice for graduate or advanced undergraduate courses, as well as for researchers and professionals who want get acquainted with this field of study. … Overall, the authors hit their target producing a textbook that aids in understanding the basic processes, methods, and issues for intelligent data analysis.” (Corrado Mencar, ACM Computing Reviews, April, 2011)

“The book provides a thorough introduction to data mining that covers theoretical background as well as the use of tools (KNIME and R). The book is intended as a textbook for a broad audience from graduate and advanced undergraduate students to professional data analysts. … each chapter ends with a list of references to identify relevant research. Hence, I recommend this book as an introductory text on data analysis for the intended target audience.” (Gottfried Vossen, Zentralblatt MATH, Vol. 1210, 2011)

Authors and Affiliations

  • FB Informatik und, Informationswissenschaft, Universität Konstanz, Konstanz, Germany

    Michael R. Berthold

  • Intelligent Data Analysis & Graphical, Models Research Unit, European Centre for Soft Computing, Mieres, Spain

    Christian Borgelt

  • FB Wirtschaft, Ostfalia University of Applied Sciences, Wolfsburg, Germany

    Frank Höppner

  • FB Informatik, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany

    Frank Klawonn

Bibliographic Information

  • Book Title: Guide to Intelligent Data Analysis

  • Book Subtitle: How to Intelligently Make Sense of Real Data

  • Authors: Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn

  • Series Title: Texts in Computer Science

  • DOI: https://doi.org/10.1007/978-1-84882-260-3

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag London Limited 2010

  • Softcover ISBN: 978-1-4471-2572-3Published: 05 September 2012

  • eBook ISBN: 978-1-84882-260-3Published: 23 June 2010

  • Series ISSN: 1868-0941

  • Series E-ISSN: 1868-095X

  • Edition Number: 1

  • Number of Pages: XIII, 394

  • Number of Illustrations: 63 b/w illustrations, 78 illustrations in colour

  • Topics: Artificial Intelligence

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