5.0 credits
30.0 h + 0.0 h
1q
Teacher(s)
Meskens Nadine ;
Language
Français
Main themes
- Introduction to Data Mining
- Knowledge discovery process
- Decision tree : algorithms CART and ID3
- Cross-validation, bootstrap
- Tree pruning
- bagging, boosting, arcing
- Random forest
- ROC curves
- Market basket analysis
- Neural network
- Cluster analysis : Hierarchical methods, K-means
- Rough sets
- Trends in data mining
- Software : TANAGRA et SAS enterprise Miner
- Applications
Evaluation methods
Oral examination
Teaching methods
- Lectures
- Course-related exercises
- Use of software
- Case studies
Bibliography
- HAN J., KAMBER M. (2006), Data mining:concepts and techniques, 2nd ed.Morgan Kaufmann.
- TUFFERY S. (2007), Data Mining et statistique décisionnelle :l'intelligence dans les bases de données, Technip.
Faculty or entity<
