Notes of CSE591

Block 1 Introduction (1 week)

• About CSE 591
• Format
• Assignment 1
• Data mining
• What is it
• Why now and what can we gain
• Data warehousing and Web mining
• What are to be covered

Block 2 Classification (2 weeks)

• Data and its format
• What is the problem of classification?
• How to learn a classifier?
• What are the representative approaches?
• What are the key issues?
• Paper presentations
• k-NN and Naive Bayes Classification
• Neural networks

Block 3 Performance Evaluation and Comparison (1 week)

• What to evaluate?
• How to evaluate?
• Paper presentation
• Comparison

Block 4 Related Issues and Preprocessing (1 week)

• Noise, missing values, data types
• Data reduction: feature selection
• Paper presentations
• Feature discretization
• Sampling

Block 5 Clustering (1.5 weeks)

• What is clustering
• Types of clustering
• Issues of clustering
• Paper presentations
• K-means and EM
• Hierarchical clustering
• BIRCH

Block 6 Association (1.5 weeks)

• Market basket analysis
• Principles of association analysis
• Paper presentations
• APRIORI
• Multi-level association rules
• Parallel association rule mining

Block 7 Data Warehousing (1 week)

• What is a data warehouse
• Basic concepts and operations
• Schemas
• Meta data
• Creating a data warehouse
• Presentation

Block 8 Web Data and Mining (2 weeks)

• Semi-structured data
• Resource description framework
• Web mining
• Content
• Structure
• Usage
• Paper presentations
• Search
• Mining

Block 9 Real-World Applications and Challenges (1 week)

• A Motorola successful application: CODEX on Nov 1, 2000, by Mr. Mike Gardner, Motorola Labs.
• Customer retention

Block 10 Project Presentation (2-3 weeks)

• Proposal presentations (1 week)
• Project presentations (2 weeks)