KNOWLEDGE DATA DISCOVERY (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Describe the functionalities and the process of knowledge data discovery; Describe the nature of data and why preprocessing is needed; Describe data mining methods; Apply different types of data mining methods; Analyze the results of data mining methods; Evaluate and modify existing knowledge data discovery process for several domain applications.

Topics:

1. Introduction;
2. Data and Preprocessing;
3. Association Analysis;
4. Regression and Classification;
5. Advanced Classification;
6. Model Evaluation and Selection;
7. Cluster Analysis;
8. Advanced Cluster Analysis;
9. Text Mining;
10. Deep Learning;
11. Hands on Rapid miner/WEKA;
12. Designing Knowledge Data Discovery Process;
13. Tools for Knowledge Data Discovery;
14. Hands on Rapid miner/WEKA;
15. Thesis Outline;
16. Applications and Trends in Data Mining.