Data mining & decision making

lsinf2275  2018-2019  Louvain-la-Neuve

Data mining & decision making
5 credits
30.0 h + 15.0 h
Saerens Marco;
Main themes
The course is structured around four themes
  1. Complements of data mining,
  2. Decision making,
  3. Information retrieval,
  4. Link analysis and web/graph mining .

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


Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • INFO1.1-3
  • INFO2.2-3
  • INFO5.2

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • SINF1.M4
  • SINF2.2-3
  • SINF5.2

Students completing this course successfully will be able to

  • explain quantitative and qualitative data mining methods and to apply them to decision making
  • develop a critical view of data mining techniques in specific application domains
  • master information retrieval techniques from very large data collection, possibly enriched with link structures (WEB, social networks, ...)
  • explain application of information retrieval techniques in the context of search engines and automated recommendation systems
  • implement data mining and information retrieval algorithms within standard software environments such as S-Plus, R, SAS, Weka or Matlab

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”.
The content changes from year to year, but the chapters with a * are always teached.
 * Complements of data mining
  • Principal components analysis
  • Canonical correlation analysis
  • Correspondence analysis
  • Log-linear models
  • Discriminant analysis
  • Multidimensional scaling
  • Markov and hidden Markov models
  • etc
* Decision making
  • * Dynamic programming and applications
  • * Markov decision processes and reinforcement learning
  • * Exploration/exploitation and bandit problems
  • Utility theory
  • Multi-criteria preference modeling - the Promethee method
  • Probabilistic reasoning with bayesian networks
  • Two-players game theory
  • Collective decisions
* Information retrieval
  • The basic vector-space model
  • The probabilistic model
  • Ranking web pages : PageRank, HITS, etc.
  • Collaborative recommendation models (recommender systems) .
Link analysis and web/graph mining
  • Network community detection
  • Similarity measures between nodes
  • Spectral graph partitioning and mapping
* Reputation and collaborative recommendation models
Evaluation methods
  • Two projects for 6 points on 20 or 7 points on 20 (for the two projects), depending on the size of these projects and the year
  • Oral exam : 14/20 or 13/20
Other information
Background :
  • LBIR1304 ou LFSAB1105 :  a course on probability theory and mathematical statistics,
  • LBIR1200 ou LFSAB1101 : an undergraduate course on matrix algebra
  • LFSAB1402 : a course on the basis of programming
Support de cours : transparents de l'enseignant et lectures recommandées :
  • Alpaydin (2004), "Introduction to machine learning". MIT Press.
  • Bardos (2001), "Analyse discriminante. Application au risque et scoring financier. Dunod.
  • Bishop (1995), "Neural networks for pattern recognition". Clarendon Press.
  • Bishop (2006), "Pattern recognition and machine learning". Springer-Verlag.
  • Bouroche & Saporta (1983), "L'analyse des données". Que Sais-je.
  • Cornuéjols & Miclet (2002), "Apprentissage artificiel. Concepts et algorithmes". Eyrolles.
  • Duda, Hart & Stork (2001), "Pattern classification, 2nd ed". John Wiley & Sons.
  • Dunham (2003), "Data mining. Introductory and advanced topics". Prentice-Hall.
  • Greenacre (1984), "Theory and applications of correspondence analysis". Academic Press.
  • Han & Kamber (2005), "Data mining: Concepts and techniques, 2nd ed.". Morgan Kaufmann.
  • Hand (1981), "Discrimination and classification". John Wiley & Sons.
  • Hardle & Simar (2003), "Applied multivariate statistical analysis". Springer-Verlag. Disponible à
  • Hastie, Tibshirani & Friedman (2001), "The elements of statistical learning". Springer-Verlag.
  • Johnson & Wichern (2002), "Applied multivariate statistical analysis, 5th ed". Prentice-Hall.
  • Lebart, Morineau & Piron (1995), "Statistique exploratoire multidimensionnelle". Dunod.
  • Mitchell (1997), "Machine learning". McGraw-Hill.
  • Naim, Wuillemin, Leray, Pourret & Becker (2004), "Réseaux bayesiens". Editions Eyrolles.
  • Nilsson (1998), "Artificial intelligence: A new synthesis". Morgan Kaufmann.
  • Ripley (1996), "Pattern recognition and neural networks". Cambridge University Press.
  • Rosner (1995), "Fundamentals of biostatistics, 4th ed".Wadsworth Publishing Company.
  • Saporta (1990), "Probabilités, analyse des données et statistique". Editions Technip.
  • Tan, Steinbach & Kumer (2005), "Introduction to data mining". Pearson.
  • Theodoridis & Koutroumbas (2003), "Pattern recognition, 3th ed". Academic Press.
  • Therrien (1989), "Decision, estimation and classification". Wiley & Sons.
  • Venables & Ripley (2002), "Modern applied statistics with S. Springer-Verlag.
  • Webb (2002), "Statistical pattern recognition, 2nd ed". John Wiley and Sons.
Faculty or entity

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

Title of the programme
Master [120] in Data Science Engineering

Master [120] in Computer Science and Engineering

Master [120] in Agricultural Bioengineering

Master [120] in Chemistry and Bioindustries

Master [120] in Environmental Bioengineering

Master [120] in Forests and Natural Areas Engineering

Master [120] in Statistic: General

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in data Science: Statistic

Master [120] in data Science: Information technology