EECS4412

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* class lectures

• |Lecture 1 - Introduction to Data Mining
• |Lecture 2 - Continue with Introduction, data mining concepts
• Lecture 2 example- Visualization
• Lecture 3 - Introduction to Preprocessing
• Lecture 4 - Introduction to Data Warehouses and Data Cubes, DBLEARN
• Lecture 5 - Introduction to Rough Sets
• Lecture 5+ - Approximating Answers - Guest Graham Toppin
• Lecture 6 - Association Rule Mining - Guest Aijun An
• Lecture 7 - Classification & Prediction
• Lecture 7a - Introduction to ELEM2
• Lecture 7b - 6 slides
• Lecture 8 - Introduction to Neural Networks
• Lecture 8a - More on Neural Networks
• Lecture 8b - Andrew Moore on Neural Networks
• Lecture 9 - Introduction to WEKA
• Lecture 10 - Clustering
• Lecture 11 - K NN
• Lecture 12 - Rule-Based Classification (Maisha Fariha)
• Lecture 13 - Support Vector Machines from you tube - Andrew Ng & Pat Winston
  
• Lecture 14 - Prediction and Classification with k-Nearest Neighbors (Yuping Lin)
  
• Lecture 15 - Clustering, EM algorithm (Bon Ryu)
  
• Lecture 15a - k-Means Clustering, Outliers and anomaly detection, EM algorithm
  
• Lecture 16 - Recommendation Systems: Collaborative Filtering
  
• Lecture 16 other - Recommendation Systems videos
  
• Lecture 17 Graph Mining, Social Network Analysis (Vincent Chu & Darren Rolfe)
  
• Lecture 18a Spatial/Spatio-temporal Data Mining
  
• Lecture 18b Tutorial on Geographical and Spatial Data Mining
  
• Lecture 18c Geoinformatics across disciplines
  
• Lecture 18d What's so Special about Spatial Data Mining
  
• Lecture 19 Text Classification (William Cohen)
  
• Lecture 20 Text Mining, Mining the World Wide Web (Van Le & Chang Han Chiang)
  
• Lecture 21 Bagging, Boosting and Stacking (Mingbin Xu)
  

• Potter's wheel - Raman et al.
• Cluster analysis example