Introduction to Data Mining

Introduction to Data Mining
Pang-Ning Tan, Michigan State University,
Michael Steinbach, University of Minnesota
Vipin Kumar, University of Minnesota

Table of Contents
Sample Chapters
Resources for Instructors and Students
Instructor Solution Manual
Errata (March 25, 2006)
Addison-Wesley Companion Book Site  

Contact info: dmbook@cs.umn.edu

Highlights:

  • Provides both theoretical and practical coverage of all data mining topics.
  • Includes extensive number of integrated examples and figures.
  • Offers instructor resources including solutions for exercises and complete set of lecture slides.
  • Assumes only a modest statistics or mathematics background, and no database knowledge is needed.
  • Topics covered include; predictive modeling, association analysis, clustering, anomaly detection, visualization.
 

Sample Chapters:

These sample chapters are also available at the publisher's Web site.

All files are in Adobe's PDF format and require Acrobat Reader.

Resources for Instructors and Students:

Link to PowerPoint Slides

Link to Figures as PowerPoint Slides

Links to Data Mining Software and Data Sets

Suggestions for Term Papers and Projects

Tutorials

Errata

PowerPoint Slides:

1. Introduction (lecture slides: [PPT] [PDF])

2. Data (lecture slides: [PPT][PDF])

3. Exploring Data (lecture slides: [PPT][PDF])

4. Classication: Basic Concepts, Decision Trees, and Model Evaluation (lecture slides: [ PPT][PDF])

5. Classication: Alternative Techniques (lecture slides: [PPT][PDF])

6. Association Analysis: Basic Concepts and Algorithms (lecture slides: [PPT][PDF])

7. Association Analysis: Advanced Concepts (lecture slides: [PPT][PDF])

8. Cluster Analysis: Basic Concepts and Algorithms (lecture slides: [PPT][PDF])

9. Cluster Analysis: Additional Issues and Algorithms (lecture slides: [PPT][PDF])

10. Anomaly Detection (lecture slides: [PPT][PDF])

 

Book Figures in PowerPoint Slide Format:

1. Introduction (figure slides: [PPT])

2. Data (figure slides: [PPT])

3. Exploring Data (figure slides: [PPT])  

4. Classication: Basic Concepts, Decision Trees, and Model Evaluation (figure slides: [ PPT])

5. Classication: Alternative Techniques (figure slides: [PPT])

6. Association Analysis: Basic Concepts and Algorithms (figure slides: [PPT])

7. Association Analysis: Advanced Concepts (figure slides: [PPT])

8. Cluster Analysis: Basic Concepts and Algorithms (figure slides: [PPT])

9. Cluster Analysis: Additional Issues and Algorithms (figure slides: [PPT])

10. Anomaly Detection (figure slides: [PPT])

 

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