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Databases and Datamining


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): 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.

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


Databases and Datamining