Topics
- Introduction to data mining
- Data representation and data preprocessing
- Data visualization
- Finding similar items (Massive, Chapter 3)
- Mining association rules
- Classification and regression methods
- Model selection and evaluation
- Clustering
- [Data warehouse and data cube (Han, Chapter 4 & 5)]
- Case studies on various types of data (documents, graph data, biological sequences)
- Link analysis (Massive, Chapter 5)
- Advertising on the web (Massive, Chapter 8)
- Recommendation systems: content-based systems and ollaborative filtering systems (Massive, Chapter 9)
- Big data mining (MinHash & LSH)
- Social/ethical issues in data mining; privacy-preserving data mining Ethics of data mining; intellectual ownership; privacy models; privacy preserving data mining & data publishing; risk analysis; user interfaces; interestingness & relevance; data & result visualization.
- Data minining on cloud data warehouse (such as BigQuery)