Introduction to Data Mining (Second Edition)

Classification: Some of the most significant improvements in the text have been in the two chapters on classification. The introductory chapter uses the decision tree classifier for illustration, but the discussion on many topics—those that apply across all classification approaches—has been greatly expanded and clarified, including topics such as overfitting, underfitting, the impact of training size, model complexity, model selection, and common pitfalls in model evaluation. Almost every section of the advanced classification chapter has been significantly updated. The material on Bayesian networks, support vector machines, and artificial neural networks has been significantly expanded. We have added a separate section on deep networks to address the current developments in this area. The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved.

Sample Chapters:

• Chapter 3. Classification: Basic Concepts and Techniques
• Chapter 5. Association Analysis: Basic Concepts and Algorithms
• Chapter 7. Cluster Analysis: Basic Concepts and Algorithms

Resources for Instructors and Students:

Link to PowerPoint Slides

Link to Figures as PowerPoint Slides

Links to Python Notebooks and Tutorials

Link to R Code Examples (Courtesy: Michael Hahsler)

Links to Data Mining Software and Data Sets

Suggestions for Term Papers and Projects

Tutorials

Solution Manual and Question Bank

Errata

Additional Resources

• Data Exploration (Chapter) (lecture slides: [PPT] [PDF]).
• Appendices [PDF].

PowerPoint Slides:

1. Introduction [PPT] [PDF] (Update: 09 Sept, 2020).
2. Data [PPT] [PDF] (Update: 27 Jan, 2021).
3. Classification: Basic Concepts and Techniques
4. Basic Concepts and Decision Trees [PPT] [PDF] (Update: 01 Feb, 2021).
5. Model Overfitting [PPT] [PDF] (Update: 03 Feb, 2021).

• Rule-based Classifier [PPT] [PDF] (Update: 30 Sept, 2020).
• Nearest Neighbor Classifiers [PPT] [PDF] (Update: 10 Feb, 2021).
• Naïve Bayes Classifier [PPT] [PDF] (Update: 08 Feb, 2021).
• Artificial Neural Networks [PPT] [PDF] (Update: 22 Feb, 2021).
• Support Vector Machine [PPT] [PDF] (Update: 17 Feb, 2020.
• Ensemble Methods [PPT] [PDF] (Update: 11 Oct 2021).
• Class Imbalance Problem [PPT] [PDF] (Update: 15 Feb, 2021).

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])

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])