DATA MINING (2/2 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to:  Explain concept of data and data preprocessing; Apply various data mining techniques; Apply data mining trends and research frontiers.

Topics :

1. Introduction to R programming;
2. Data visualization in R programming;
3. Data description using R programming;
4. Data preprocessing;
5. Mining frequent pattern and associations;
6. Quiz;
7. Clustering I;
8. Clustering II;
9. Classification I;
10. Classification II;
11. Outlier detection;
12. Introduction;
13. Getting to know your data;
14. Data visualization and preprocessing I;
15. Data preprocessing II;
16. Mining frequent patterns, associations, and correlations I;
17. Mining frequent patterns, associations, and correlations II;
18. Cluster analysis I;
19. Cluster analysis II;
20. Classification I;
21. Classification I;
22. Data mining trends and research frontiers Outlier analysis I;
23. Outlier analysis I;
24. Outlier analysis II.

Course Outline – COMP6140