Admission requirements
C++, Databases
Description
The course Databases & Data Mining consists of a series of lectures in which advanced database and data mining techniques will be discussed, with applications to bioinformatics.
Course objectives
At the end of the course, students:
Should have a clear understanding of the current challenges and state of the art of databases and data mining.
Will have an understanding of the basic algorithms for preparing data and databases for data warehousing and data mining.
Will understand the basic data structures and organization that enable data analysis and data mining huge data sets.
Have an understanding of the important algorithms and challenges in several important emerging applications of data mining: mining biosequence databases, social networks, and graph mining.
Timetable
The most recent timetable can be found at the LIACS website
Mode of instruction
Lectures
Panel presentations and discussions.
Assignments
Reports
Assessment method
There will be a total of 4 database- and data mining assignments and a final exam (open book). The assignments P1 and P2 should both be passed with a ‘good’. Subsequently, the final grade will be based on a weighted average of the grades obtained for assignments P3, P4 and the Exam (E): Final Grade = (P3 +2*P4 + 3*E)/6.
Reading list
J. Han, M. Kamber, J. Pei. Data Mining Concepts and Techniques (3rd Edition), Morgan Kaufman Publishers, July 2011 (ISBN 978-0123814791)
Registration
You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.
There is a limited capacity for students from outside the master Computer Science programme. Please contact the study advisor.
Contact information
Study coordinator Computer Science, Riet Derogee