Data Mining

mlsmm2151  2024-2025  Mons

Data Mining
5.00 credits
30.0 h
Q1
> Schedule
Teacher(s)
Ait El Cadi Abdessamad;
Language
French
Prerequisites
Statistics
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
Learning outcomes

At the end of this learning unit, the student is able to :

1 At the end of this learning unit, the student is able to:
  • Extract knowledge contained in large volumes of data from real data and using data mining software such as SAS enterprise Miner and TANAGRA;
  • Interpret the results provided by such software;
  • Describe the principles of supervised and unsupervised learning methods seen in the course;
  • Use the appropriate methods to deal with a given problem;
  • Read and understand research articles related to a management problem and using data mining methods.
 
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
> CLSM


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] : Business Engineering
INGE2M
5
Master [120] : Business Engineering
INGM2M
5
Master [120] in Management (with work-linked-training)
GESA2M
5