DATA MINING (2/2 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: cite concept of data, data preprocessing, data warehouse, OLAP, and data cube; apply various data mining techniques; apply data mining trends and research frontiers.

Topics :

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