WPI Worcester Polytechnic Institute

Computer Science Department
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CS525D Knowledge Discovery and Data Mining 
Schedule of Classes - Fall 2009

PROF. CAROLINA RUIZ 

WARNING: Changes to this schedule may be made during the course of the semester. 
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WEEK DATE DUE TOPIC READINGS
1 Sept. 8 & 10   Introduction to KDD & Data Mining   Chp. 1 & 9, assigned papers
2 Sept. 15 & 17   Data & Data Preparation
  • Concepts, instances, attributes
  • Data integration
  • Data preprocessing
  •   2, 7.1-7.3, 10.1-10.3, 10.8
    3 Sept. 22 & 24   Data & Data Preparation (cont.)
  • Data warehousing & OLAP
  • Attribute selection
  • Dimensionality reduction
  •   2, 7.1-7.3, 10.1-10.3, 10.8
    4 Sept. 29 & Oct. 1 Project 1 Mining process
  • Training and Testing
  • Cross validation
  • Performance evaluation
    Project 1 presentations
  •   5.1-5.4, 5.8
    5 Oct. 6 & 8   Classification
  • Decision trees
  •   3.2, 4.3, 6.1
    6 Oct. 13 & 15   Numeric Predictions
  • model trees
  • regression trees
  •   3.7, 4.6, 6.5, 5.8
    7 Oct. 20 & 22 Project 2 Project 2 presentations
    Association Analysis
  • association rules
  •   3.4, 4.5, assigned papers
    8 Oct. 27 & 29   Association Analysis (cont.)
  • association rules
  •   3.4, 4.5, assigned papers
    9 Nov. 3 & 5 Project 3 Cluster Analysis
  • partitioning methods
  • hierarchical methods
  • density-based methods
    Project 3 presentations
  •   3.9, 4.8, 6.6
    10 Nov. 10 & 12   Cluster Analysis (cont.)
  • grid-based methods
  • model-based methods
  •   3.9, 4.8, 6.6
    11 Nov. 17 & 19 Project 4 Anomaly Detection
  • model-based methods
  • proximity-based methods
  • density-based methods
    Project 4 presentations
  •   7.4, assigned papers
    12 Dec. 1 & 3 Project 5 Advanced topics
  • Visualization
  • Web mining
    Project 5 presentations
  •   8, assigned papers
    13 Dec. 8 & 10   Advanced topics (cont.)
  • Similarity search
  • Sequence mining
  • Multimedia data mining
  •   8, assigned papers
    14 Dec. 15 & 17 Project 6 Advanced topics (cont.)
  • Text mining
  • Industrial applications of data mining
  • Scientific applications of data mining
    Project 6 presentations
  •   8, assigned papers