Knowledge Data Discovery

Learning Outcomes

On successful completion of this Course, students will be able to: explain basic concepts of data mining; design and computation of data cubes; implement data processing and understand data modeling services; compare analysis techniques on data mining; implement analysis techniques in data mining; explain trends and applications associated with data mining.

Topics

1. Introduction;
2. Data Warehousing, Data Generalization, and Online Analytical Processing;
3. Data Preprocessing;
4. Data Cube Computation and Data Generalization;
5. Mining Frequent Patterns, Association, and Correlations;
6. Classification and Prediction;
7. Cluster Analysis;
8. Graph Mining, Social Network Analysis, and Multirelational Data Mining;
9. Implementations: Real machine learning schemes;
10. Applications and Trends in Data Mining;
11. The Weka machine learning workbench.