Data mining & decision making [ LSINF2275 ]

> https://www.icampus.ucl.ac.be/claroline/course/index.php?cid=SINF2275

• A first course on probability theory
• A first course on mathematical statistics
• An undergraduate course on matrix algebra
• An undergraduate course on multivariate analysis

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 .

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

Complements of data mining

• Canonical correlation analysis
• Correspondence analysis
• Partial least squares regression
• Log-linear models
• Association rules

Decision making

• 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
• Possibility theory
• 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 models

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• 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 http://www.quantlet.com/mdstat/scripts/mva/htmlbook/mvahtml.html
• 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.