Lectures Data Mining Part 1. Lecture1: Introduction to Data Mining. October 5 (2 hours) 2. Lecture2: Getting to Know Your Data. October 12 (2 hours) 3. Lecture3: Probability Theory and Statistics. October 15 (2 hours) 4. Lecture4: Classification and Prediction -Decision Trees. October 19 (2 hours) 5. Lecture5: Classification and Prediction -Decision Tree-Bayes. October 22 (2 hours) 6. Lecture6: Classification and Prediction -RuleBased-LazyClassifiers. October 26 (2 hours) 7. Lecture7: Classification and Prediction -Accuracy. October 29 (2 hours) 8. Lecture8: Cluster Analysis: Partitioning Algorithms . November 2 (2 hours) 9. Lecture9: Cluster Analysis: Hierarchical and Density-based Algorithms. November 5 (2 hours) ppt 10. Lecture10: Cluster Analysis: High-Dimensional Clustering and Outlier Analysis. November 9 (2 hours) 11. Lecture11-12: Frequent Pattern Mining. November 12, November 15 (4 hours) ppt

Lectures

Data Mining Part

1.       Lecture1: Introduction to Data Mining. October 5 (2 hours)