Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h
Q1
Teacher(s)
Lebichot Bertrand (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
- Lectures
- Course-related exercises
- Use of software
- Case studies
Evaluation methods
Oral examination
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
