Topics of CSE591d

Block 1 Introduction (1 week)

• About CSE 591d (Spring 2002)
• Format (Learning, Thinking,  Doing, Presenting, Reporting)
• Textbooks and reference materials
• What students are expected to do:
• 2 exams (20%, 20%)
• One group topic presentation and slides (15%, 10%)
• One project presentation and report  (with demo)(10%,15%)
• Class participation (10%)
• Survey (student backgrounds, views, expectations, new areas of research)
• Data mining
• What is it
• Why now and what can we gain
• Data warehousing and Web mining
• What are to be covered
• Classification
• Clustering
• Association
• Evaluation
• Data Preparation
• New topics (Web Minng, Machine Learning, Data Seleciton, Data Streaming, ....)

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?
• Group presentations
• k-NN and Naive Bayes Classification
• Case-Based Reasoning
• Neural Networks
• Rule Extraction from Trees and Networks
• MetaCost: cost sensitive classification

Block 3 Performance Evaluation and Comparison (1 week)

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

Block 4 Data Preparation and Preprocessing (1 week)

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

Block 5 Clustering (2 weeks)

• What is clustering
• Types of clustering
• Issues of clustering
• Paper presentations
• Partitioning: K-means and EM
• Hierarchical clustering: BIRCH
• Density-based: DBSCAN
• Grid-based: STING

Block 6 Association (2 weeks)

• Market basket analysis
• Principles of association analysis
• Group presentations
• APRIORI in detail
• Frequent ITemsets without Candidate Generation
• Multi-level association rules
• Constraint-based Association Mining
• 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
• Group Presentation

Block 8 Web Data and Mining (2 weeks)

• Semi-structured data (XML)
• Resource description framework (RDF)
• Data Streaming
• Web mining
• Content
• Structure
• Usage
• Group presentations
• Search
• Mining
• Personalization

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

• Image Mining
• Customer retention

Block 10 Project Presentation (2 weeks)

• Project presentations (2 weeks)