Items related to Guide to Intelligent Data Analysis: How to Intelligently...
Synopsis
Introduction Practical Data Analysis: An Example Project Understanding Data Understanding Principles of Modeling Data Preparation Finding Patterns Finding Explanations Finding Predictors Evaluation and Deployment Appendix A: Statistics Appendix B: The R Project Appendix C: KNIME
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Review
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)
About the Author
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 Hoppner 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.
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- PublisherSpringer
- Publication date2010
- ISBN 10 1848822677
- ISBN 13 9781848822672
- BindingPaperback
- LanguageEnglish
- Number of pages410
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