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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 students' website.

Mode of instruction

  • Lectures

  • Panel presentations and discussions.

  • Assignments

  • Reports

Course Load

Hours of study: 168 (= 6 EC)
Lectures: 26
Practical work: 40
Exam: 22
Other (Self-study): 80

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 courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

Contact information

Lecturer: dr. Erwin Bakker
Website: Databases and Datamining