Data Warehousing and Data Mining
This is a jupyter book for the course CS5483 Data Warehousing anad Data Mining at City University of Hong Kong. The course aims to equip students with the fundamental concepts in data science and hands-on experience working with some real-world datasets.
Topics covered:
- Predictive modeling: Classification, performance evaluation, decision tree, information theory, ensemble methods, ...
- Cluster analysis: partitional, hierarchical, density-based methods, ...
- Association-rule mining: market basket analysis, Apriori algorithm, ...
- Data warehousing: data cube, OLAP, ...
Course Intended Learning Outcomes (CILOs):
- Identify the main characteristics of different data warehousing and data mining techniques through observation of their operations.
- Perform a critical assessment of current data warehousing and data mining techniques.
- Implement the main algorithms in data warehousing and data mining in a computationally efficient way.
- Propose new solutions for data warehousing and data mining problems by improving and combining current techniques.