Mathematics Study Program – Mathematics Department – Faculty of Science – University of Brawijaya
3 sks. Odd Semester 2008/2009 – Requirement: Data Base

Schedule :
Tuesday, 13:00 – 14:45 - BM 2.1 & Thursday, 14:50 – 15:50 - MP 2.4

Aims of course :
Students can understand and use data mining techniques for mining information in large database .

Course Outline:

• Introduction to Data Mining
• Data Pre processing
• Dimension Reduction Method: Entropy Measure For Ranking Features, PCA
• Data Pre processing
• Clustering: K-means, Fuzzy clustering, Hierarchical Clustering, Self Organizing Feature Maps
• Association-Rule Mining
• Classification: Decision Tree, Bayesian, Discriminant Analysis
• Information Extraction Using Neural Networks
• Prediction: Support Vector Machines
• Case Study
  

Text Book :

• Larose , D. T., 2006. Data Mining Methods And Models. John Wiley & Sons, Inc.
• Han, J. and Micheline K., 2006.Data Mining: Concepts and Techniques Second Edition, Elsevier Inc.
• Berry, M. J.A. and Gordon S. Linoff, 2004, Data Mining Techniques For Marketing, Sales, and Customer Relationship Management, Wiley Publishing, Inc.
• Larose , D. T., 2005, DISCOVERING KNOWLEDGE IN DATA : An Introduction to Data Mining, John Wiley & Sons, Inc.
  

Evaluation:

1. Quiz : 30%
2. Assignment : 20%
3. Middle Test : 25%
4. Final Test ( Case Study / Presentation) : 25%