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Intelligent Data Analysis

An Introduction

  • Book
  • © 1999

Overview

Editors:
  1. Michael Berthold

    1. Berkeley Initiative in Soft Computing, Computer Science Division, University of California, Berkeley, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. David J. Hand

    1. Department of Statistics, The Open University, Milton Keynes, UK

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    You can also search for this editor in PubMed   Google Scholar

  • First monograph coherently presenting the various approaches to intelligent data analysis Valuable source of reference for professionals Excellent textbook for students and IT professionals Numerous illustrations and examples

  • Includes supplementary material: sn.pub/extras

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Table of contents (10 chapters)

  1. Front Matter

    Pages I-IX
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  2. Introduction

    • David J. Hand
    Pages 1-14

  3. Statistical Concepts

    • Ad J. Feelders
    Pages 15-66

  4. Statistical Methods

    • Paul Taylor
    Pages 67-127

  5. Bayesian Methods

    • Marco Ramoni, Paola Sebastiani
    Pages 129-166

  6. Analysis of Time Series

    • Elizabeth Bradley
    Pages 167-194

  7. Rule Induction

    • Gerard C. van den Eijkel
    Pages 195-216

  8. Neural Networks

    • Rosaria Silipo
    Pages 217-268

  9. Fuzzy Logic

    • Michael Berthold
    Pages 269-298

  10. Stochastic Search Methods

    • Christian Jacob
    Pages 299-350

  11. Systems and Applications

    • Xiaohui Liu
    Pages 351-364

  12. Back Matter

    Pages 365-401
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Keywords

About this book

The obvious question, when confronted with a book with the title of this one, is why "intelligent" data analysis? The answer is that modern data analysis uses tools developed by a wide variety of intellectual communities and that "intelligent data analysis" , or IDA, has been adopted as an overall term. It should be taken to imply the intelligent application of data analytic tools, and also the application of "intelligent" data analytic tools, computer programs which probe more deeply into structure than first generation methods. These aspects reflect the distinct influences of statistics and machine learning on the subject matter. The importance of intelligent data analysis arises from the fact that the modern world is a data-driven world. We are surrounded by data, numerical and otherwise, which must be analysed and processed to convert it into infor­ mation which informs, instructs, answers, or otherwise aids understanding and decision making. The quantity of such data is huge and growing, the number of sources is effectively unlimited, and the range of areas covered is vast: industrial, commercial, financial, and scientific activities are all generating such data.

Editors and Affiliations

  • Berkeley Initiative in Soft Computing, Computer Science Division, University of California, Berkeley, USA

    Michael Berthold

  • Department of Statistics, The Open University, Milton Keynes, UK

    David J. Hand

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