Advanced Data mining

Pre-requisites : DataBase, Statistics
Organization : 7 x 3h Lectures/Turorials
Évaluation : Interaction, project, exam
ECTS : 2,5 crédits

Responsible: Raja CHIKY

Contexte

Data Mining also known as "knowledge data extraction" is a set of algorithms to analyze and explore data, to better understand trends or make predictions. Data Mining is applied to a wide number of business areas such as health (diagnostic aid), trade (analysis of buying behavior), banking / finance (credit card fraudulent use detection), etc. The process of Data Mining includes the data selection, the cleaning, the use of different statistical techniques and machine learning, and the visualization of extracted knowledge. The course will address the technologies such as data streaming, web/text mining, association rules and recommender systems.

Objectives

Skills

This module allows the students to be familiar with methods and algorithms from statistics and artificial intelligence applied in Data Mining. They will study all the different steps of the knowledge extraction from raw data. We will introduce methods related to data stream mining, web mining in application to recommender systems, text mining, and association rules.

Knowledge

Concepts

  • Data Stream mining
  • Association rules
  • Web Mining
  • Text Mining
  • Recommender systems

Know-How

  • Gain knowledge in the field of data stream
  • Use a data stream management system
  • Understand the concepts under recommendation systems
  • Build a recommender system
  • Know how to use some methods of extracting association rules
  • Use knowledge data extraction tools (R, Weka, RapidMiner, etc.)

Pedagogical approach

The module is organized as follows: a lecture or a tutorial per week. Three sessions will be devoted to the practice through these tutorials. Students have to realize a final project related to the implementation of advanced data mining tools or to a bibliographic research.

References

  • Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition), Morgan Kaufmann, 2005, ISBN: 0-12-088407-0.
  • Han J., Kamber M. ; Data Mining Concepts and Techniques ; Morgan Kaufmann, 2011.
  • The element of statistical learning: Data Mining, Inference and prediction. T. Hastie, R. Tibshirani, J. Friedman, 2009 (2nd edition)
  • Fundamentals of Predictive Text Mining. S.M Weiss, N. Indurkhya, T.Zhang, Springer, 2010