Intelligent Data Analysis

1. Front Matter

   Pages I-XI

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2. Introduction

   • David J. Hand

   Pages 1-15

3. Statistical Concepts

   • Ad J. Feelders

   Pages 17-68

4. Statistical Methods

   • Paul Taylor

   Pages 69-129

5. Bayesian Methods

   • Marco Ramoni, Paola Sebastiani

   Pages 131-168

6. Support Vector and Kernel Methods

   • Nello Cristianini, John Shawe-Taylor

   Pages 169-197

7. Analysis of Time Series

   • Elizabeth Bradley

   Pages 199-227

8. Rule Induction

   • Peter Flach, Nada Lavrač

   Pages 229-267

9. Neural Networks

   • Rosaria Silipo

   Pages 269-320

10. Fuzzy Logic

    • Michael R. Berthold

    Pages 321-350

11. Stochastic Search Methods

    • Christian Jacob

    Pages 351-401

12. Visualization

    • Daniel Keim, Matthew Ward

    Pages 403-427

13. Systems and Applications

    • Xiaohui Liu

    Pages 429-443

14. Back Matter

    Pages 445-514

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This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.

• Bayesian analysis
• Data Mining
• Intelligent Data Analysis
• Machine Learning
• Neuro-Fuzzy Data Analysis
• Statistical Data Analysis
• data visualization
• fuzzy logic
• inductive logic programming
• multi-variate Analysis
• statistical inference
• statistical learning
• time series analysis
• artificial intelligence
• data analysis
• intelligence
• kernel
• logic
• neural networks
• Support Vector Machine

From the reviews of the second edition:

"One excellent feature of the second addition … . This is a particularly nice overview with excellent descriptions and numerous illustrations, most in color, for a wide variety of types of visualizations. " (E. Ziegel, Technometrics, 2005)

"In this second edition … have expanded the coverage of topics and ensured that this remains the key text for surveying the field. The twelve chapters which make up the book provide an academically rigorous and concise to the key methodologies which make up the discipline. … In all this is a comprehensive survey of the field, and will appeal to graduate and post-graduate students, researchers and academics seeking an overview of the theoretical tools available for intelligently analyzing large, complex data sets." (TechBookReport, November, 2003)

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

  Michael Berthold

• Department of Mathematics, Imperial College, London, UK

  David J. Hand

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