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)