Advanced Data Mining Techniques

1. Front Matter

   Pages I-XII

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • David L. Olson, Dursun Delen

      Pages 3-8

   3. Data Mining Process

      • David L. Olson, Dursun Delen

      Pages 9-35

3. Data Mining Methods As Tools

   1. Front Matter

      Pages 37-37

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   2. Memory-Based Reasoning Methods

      • David L. Olson, Dursun Delen

      Pages 39-52

   3. Association Rules in Knowledge Discovery

      • David L. Olson, Dursun Delen

      Pages 53-68

   4. Fuzzy Sets in Data Mining

      • David L. Olson, Dursun Delen

      Pages 69-86

   5. Rough Sets

      • David L. Olson, Dursun Delen

      Pages 87-109

   6. Support Vector Machines

      • David L. Olson, Dursun Delen

      Pages 111-123

   7. Genetic Algorithm Support to Data Mining

      • David L. Olson, Dursun Delen

      Pages 125-135

   8. Performance Evaluation for Predictive Modeling

      • David L. Olson, Dursun Delen

      Pages 137-147

4. Applications

   1. Front Matter

      Pages 149-149

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   2. Applications of Methods

      • David L. Olson, Dursun Delen

      Pages 151-167

5. Back Matter

   Pages 169-180

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The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve unc- tain description or other complexities that cause difficulty for the conv- tional approaches of logistic regression, neural network models, and de- sion trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Book Concept Our intent is to cover the fundamental concepts of data mining, to dem- strate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Part I: Introduction Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided.

• Data Mining
• Data Mining Applications
• Data Mining Process
• Rough Sets
• Service-oriented Computing
• Support Vector Machine
• Support Vector Machines
• knowledge discovery
• modeling
• sets

From the reviews:

"Text analysis and data mining have become increasingly important capabilities in today’s information-flooded world, and choosing the right technique makes all the difference. This book contains some advanced data mining techniques, but also includes an overview of important data mining fundamentals, specifically the CRISP-DM and SEMMA industry standards. … Summing Up: Recommended. Upper-division undergraduates and up." (H. J. Bender, CHOICE, Vol. 45 (11), August, 2008)

• Department of Management Science, University of Nebraska, Lincoln, USA

  David L. Olson

• Department of Management, Science and Information Systems, Tulsa, USA

  Dursun Delen

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