TGGS > ECE > 512
Principles of Data Mining
Principles and algorithms of data mining. Data cleaning and integration. Descriptive and predictive mining. Frequent, sequential and structured pattern mining. Clustering. Outlier analysis and fraud detection. Other research topics in data mining.
Credits : 3 (3-0-6)
Pre-Requisites : No
| Course Learning Outcomes (CLOs) : | |
|---|---|
| CLO 1 | Explain and analyze basic principles for data preparation and management including data cleaning, data transformation and data warehousing in the context of data mining |
| CLO 2 | Explain the basic principles of frequent pattern mining, and apply the mining method for effective data mining |
| CLO 3 | Demonstrate proficiency in theoretical principals in the context of data mining |
| CLO 4 | Identify, analyze and modify existing methods in data mining in various context |
| CLO 5 | Design and apply data mining methods to solve problems in real-world context and communicate result |
| CLO1 | CLO2 | CLO3 | CLO4 | CLO5 | |
| ELO 2 | ✓ | ✓ | ✓ | ✓ | |
| ELO 3 | ✓ | ✓ | |||
| ELO 6 | ✓ | ||||
| ELO 7 | ✓ | ✓ | |||
| ELO 8 | ✓ | ✓ |
Revision : July 2021 (090245340)
Other Revisions : July 2020