DATA MINING (2/2 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to:

1. Cite concept of data, data preprocessing, data warehouse, OLAP, and data cube
2. Apply various data mining techniques
3. 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, Online Analytical Processing, Data Cube Technology
23. Cluster analysis I
24. Cluster analysis II