Data Bases and Data Mining – Computer Science Department

Databases and Data Mining

Course offered in the second semester of the M1.

This course gives a general introduction of data management through relational databases and data mining from algorithms to theoretical aspects.

Databases:

Relational model, relational calculus, SQL
Relational algebra, equivalence between relational calculus and algebra, indexation and optimisation
Functional dependencies, axiomatisation, Armstrong’s relations
Inclusion dependencies, data exchange, chase, query rewriting
Openness to current issues

Data Mining:

Introduction to data mining (itemset/rule mining, enumeration algorithms)
constraint-based pattern mining: study of constraint properties and pruning
Assessment of results (statistical significance, relevancy w.r.t. background knowledge, user prior)
Advanced pattern mining: formal concept analysis, sequences, graphs, dynamics graphs, attributed graphs
Clustering: general aims, distances, similarities, algorithms (kmeans, EM, density-based clustering, spectral clustering, hierarchical clustering), graph clustering
Anomaly detection
Current issues and challenges

Teaching methods: Lectures and Lab sessions (TP).
Form(s) of Assessment: written exam (50%) and project(s) (50%)

M. Levene, G. Loizou. A Guided Tour of Relational Databases and Beyond. Springer, 1999.
I S. Abiteboul, R. Hull et V. Vianu. Fondements des bases de données. Addison-Wesley, 1995.
I R. Ramakrishnam et J. Gehrke. Database Management Systems. 2003 (third edition).

Material available on pages.cs.wisc.edu/~dbbook/)
On line course: https://cs.stanford.edu/people/widom/DB-mooc.html
Charu C. Aggarwal, Data Mining, Springer, 2015.
Mohammed J. Zaki, Wagner Meira, Jr. Data Mining and Analysis Fundamental Concepts and Algorithms. Cambridge University Press, 2014.