Data Mining II

5 credits

Syllabus, Master's level, 1DL460

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Computer Science A1F, Technology A1F
Grading system
Pass with distinction, Pass with credit, Pass, Fail
Finalised by
The Faculty Board of Science and Technology, 30 August 2018
Responsible department
Department of Information Technology

Entry requirements

120 credits including Data Mining I and Database Design II. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

  • explain advanced methods for data mining from large amounts of data, both in theory and in practical application
  • implement and use these methods with appropriate tools and
  • evaluate and compare the suitability, scalability and efficiency of different methods.


Advanced methods in data mining covering the fields of web data mining and search engines, spatial and temporal data mining including cluster evaluation and advanced clustering, stream based data mining, and sequential association analysis. Additional areas include advanced association analysis, methods for large-scale data mining, introduction to outlier analysis, and methods for privacy-preserving in data mining.


Lectures, seminars, laboratory sessions and assignments. Guest lecture


Written examination (3 HE credits) and written and oral presentation of assignments (2 HE credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course can not be counted in higher education qualification together with the course Data Mining (7.5 HE credits).