Data Mining

Learning Outcomes

On successful completion of this course, student will be able to: Explain data mining concepts and techniques; Analyze collection of data and techniques for pre-processing the data before mining; Analyze case studies and design data mining techniques to solve problems by extracting knowledge from data; Assess trends and applications related to data mining.

Topics

1. Introduction/Overview of Data Mining
2. Getting to Know Your Data
3. Data Pre-processing
4. Classification: Basic Concepts – Decision Tree Induction
5. Classification: Basic Concepts – Rule-Based Classification
6. Classification: Basic Concepts – Bayes Classification Methods
7. Classification: Advanced Methods – Lazy Learners (or Learning from Your Neighbors)
8. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
9. Advanced Pattern Mining
10. Advanced Pattern Mining (2)
11. Cluster Analysis: Basic Concepts and Methods
12. Advanced Cluster Analysis
13. Advanced Cluster Analysis (2)
14. Outlier Detection
15. Data Mining Trends and Research Frontiers