Data Mining: A Tutorial Based Primer / Edition 1

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Product Details

  • ISBN-13: 9780201741285
  • Publisher: Pearson
  • Publication date: 9/26/2002
  • Edition description: BK&CD-ROM
  • Edition number: 1
  • Pages: 408
  • Sales rank: 1,234,100
  • Product dimensions: 7.30 (w) x 9.00 (h) x 0.70 (d)

Meet the Author

Richard J. Roiger is a professor of computer science at Minnesota State University, Mankato and a senior software engineer for Information Acumen Corporation ( Richard received a Ph.D. degree in Computer Science from the University of Minnesota in 1991. His research interests include machine learning, knowledge discovery in databases and expert systems. He is a member of the American Association of Artificial Intelligence, the Association for Computing Machinery and IEEE. He is also a musician and songwriter, enjoys spending time with his children and grandchildren and likes playing golf as time permits.

Michael W. Geatz is currently President of Biosensor Research Institute of America Inc. (dba Giant Medical). Formerly, he was Vice President of PulseTracer Technologies Inc., a division of $1.5 billion Zynik Capital Corp. and a software consultant to the financial and medical device industries. He was also co-founder of an artificial intelligence company, Information Acumen Corp., is a named inventor of a patented piezoelectric switch for use in radio frequency identification (RFID) chips as well as the world's first wrist pulse sensor, and is proud of his civilian service to the U.S. Army during the first Gulf war. Educational credentials include an MBA from Golden Gate University in 1991 and a Computer Science degree from University of North Dakota in 1984.

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Table of Contents

(Each Chapter concludes with a Chapter Summary, Key Terms, and Exercises.)



1. Data Mining: A First View.

Data Mining: A Definition.

What Can Computers Learn?

Is Data Mining Appropriate for my Problem?

Expert Systems or Data Mining?

A Simple Data Mining Process Model.

Why not Simple Search?

Data Mining Applications.

2. Data Mining: A Closer Look.

Data Mining Strategies.

Supervised Data Mining Techniques.

Association Rules.

Clustering Techniques.

Evaluating Performance.

3. Basic Data Mining Techniques.

Decision Trees.

Generating Association Rules.

The K-Means Algorithm.

Genetic Learning.

Choosing a Data Mining Technique.

4. An Excel-Based Data Mining Tool.

The iData Analyzer.

ESX: A Multipurpose Tool for Data Mining.

iDAV Format for Data Mining.

A Five-Step Approach for Unsupervised Clustering.

A Six-Step Approach for Supervised Learning.

Techniques for Generating Rules.

Instance Typicality.

Special Considerations and Features.


5. Knowledge Discovery in Databases.

A KDD Process Model.

Step 1: Goal Identification.

Step 2: Creating a Target Data Set.

Step 3: Data Preprocessing.

Step 4: Data Transformation.

Step 5: Data Mining.

Step 6: Interpretation and Evaluation.

Step 7: Taking Action.

The CRISP-DM Process Model.

Experimenting with ESX.

6. The Data Warehouse.

Operational Databases.

Data Warehouse Design.

On-line Analytical Processing (OLAP).

Excel Pivot Tables for Data Analysis.

7. Formal Evaluation Techniques.

What Should be Evaluated?

Tools for Evaluation.

Computing Test Set Confidence Intervals.

Comparing Supervised Learner Models.

Attribute Evaluation.

Unsupervised Evaluation Techniques.

Evaluating Supervised Models with Numeric Output.


8. Neural Networks.

Feed-Forward Neural Networks.

Neural Network Training: A Conceptual View.

Neural Network Explanation.

General Considerations.

Neural Network Learning: A Detailed View.

9. Building Neural Networks with iDA.

A Four-Step Approach for Backpropagation Learning.

A Four-Step Approach for Neural Network Clustering.

ESX for Neural Network Cluster Analysis.

10. Statistical Techniques.

Linear Regression Analysis.

Logistic Regression.

Bayes Classifier.

Clustering Algorithms.

Heuristics or Statistics?

11. Specialized Techniques.

Time-Series Analysis.

Mining the Web.

Mining Textual Data.

Improving Performance.


12. Rule-Based Systems.

Exploring Artificial Intelligence.

Problem Solving as a State Space Search.

Expert Systems.

Structuring a Rule-Based System.

13. Managing Uncertainty in Rule-Based Systems.

Uncertainty: Sources and Solutions.

Fuzzy Rule-Based Systems.

A Probability-Based Approach to Uncertainty.

14. Intelligent Agents.

Characteristics of Intelligent Agents.

Types of Agents.

Integrating Data Mining, Expert Systems, and Intelligent Agents.


Appendix A: Software Installation.

Appendix B: Datasets for Data Mining.

Appendix C: Decision Tree Attribute Selection.

Appendix D: Statistics for Performance Evaluation.

Appendix E: Excel 97 Pivot Tables.


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