Data Mining [ MQANT2113 ]
5.0 crédits ECTS
30.0 h + 0.0 h
1q
| Teacher(s) |
Meskens Nadine ;
|
| Language |
French
|
Place
of the course |
Mons
|
| 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
|
| Aims |
The contribution of this Teaching Unit to the development and
command of the skills and learning outcomes of the programme(s) can be
accessed at the end of this sheet, in the section entitled
“Programmes/courses offering this Teaching Unit”.
|
| Evaluation methods |
Oral examination
|
| Teaching methods |
-
Lectures
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Course-related exercises
-
Use of software
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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
in charge |
> BLSM
|
|
|
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