The past few years have witnessed a surge in the quantity of stocked data (over 80% of the data in the world today has been created in the past 3 years alone). The rush to extract valuable information from this data has led to a 500% increase in the big data market, which made data mining an increasingly demanded transversal knowledge and skill worldwide. The aim of this course is to introduce data mining and its applications and dive into the different types of data, preprocessing techniques as well as the major approaches to analyze each type of data with direct applications in Rstudio and emphasis on the visual representation of the obtained results.

Temps présentiel : 23.75 heures

Charge de travail étudiant : 76.25 heures

Méthode(s) d'évaluation : Projets

Référence :
Christopher Michael Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, Springer-Verlag New York, Inc., Secaucus, NJ, USA., 2006 Jiawei Han and Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques. The Morgan Kaufmann series in data management systems, Morgan Kaufmann, California, Amsterdam, Boston, Heidelberg, 2006 Mohammed Zaki and Wagner Meira Jr, Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, New York, 2014 Julia Silge and David Robinson, Text Mining with R : A Tidy Approach, O'Reilly Media, 2017 Wissam Samrouni, Data Mining with R., Paris, 2017 Agrawal,R. and David Robinson, Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, 1994 Mannila H. and Hannu Toivonen and A. Inkeri Verkamo, Discovery of Frequent Episodes in Event Sequences"". Data Min. Knowl. Discov., 1997 Jiawei Han, Mining Frequent Patterns Without Candidate Generation Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000