Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h
Q1
Teacher(s)
Ait El Cadi Abdessamad (compensates Meskens Nadine); Meskens Nadine;
Language
French
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
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- 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
CLSM
