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Databases and Data Mining


Admission requirements

C++, Databases


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.


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 >5): 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)


You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

Contact information

Study coordinator Computer Science, Riet Derogee


Databases and Datamining