Data Mining

1. Front Matter

   Pages I-XV

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2. Data Mining and Knowledge Discovery Process

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 3-7

   3. The Knowledge Discovery Process

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 9-24

3. Data Understanding

   1. Front Matter

      Pages 25-25

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   2. Data

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 27-47

   3. Concepts of Learning, Classification, and Regression

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 49-67

   4. Knowledge Representation

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 69-91

4. Data Preprocessing

   1. Front Matter

      Pages 93-93

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   2. Databases, Data Warehouses, and OLAP

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 95-131

   3. Feature Extraction and Selection Methods

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 133-233

   4. Discretization Methods

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 235-254

5. Data Mining: Methods for Constructing Data Models

   1. Front Matter

      Pages 255-255

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   2. Unsupervised Learning: Clustering

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 257-288

   3. Unsupervised Learning: Association Rules

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 289-306

   4. Supervised Learning: Statistical Methods

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 307-379

   5. Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 381-417

   6. Supervised Learning: Neural Networks

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 419-451

   7. Text Mining

      • Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan

      Pages 453-465

6. Data Models Assessment

   1. Front Matter

      Pages 467-467

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“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.

• DOM
• LA
• OLAP
• algorithms
• classification
• data mining
• data security
• data warehouse
• knowledge discovery
• knowledge representation
• learning
• supervised learning
• tools
• unsupervised learning

From the reviews: “This is a comprehensive book about knowledge discovery methods. … the book is highly recommended to final year undergraduate students, postgraduate students and lecturers. … it has a good balance of various topics making it a good reference book for practitioners, such as data modellers, insight analysts, fraud analysts, etc., as well as researchers. … this book is very well organized and presented. … I would certainly recommend it to those with intermediate or advanced understanding of data-mining topics.” (Boran Gazi, The Computer Journal, Vol. 53 (4), 2010)

• Computer Science Dept, Virginia Commonwealth University, Richmond

  Krzysztof J. Cios

• University of Colorado, USA

  Krzysztof J. Cios

• Computer Science Dept, San Diego State University & Polish Academy of Sciences, San Diego, USA

  Roman W. Swiniarski

• Electrical and Computer Engineering Dept, University of Alberta, Edmonton, Canada

  Witold Pedrycz, Lukasz A. Kurgan

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