Syllabus for Data Mining II

Informationsutvinning II


  • 5 credits
  • Course code: 1DL460
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1F, Technology A1F
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2010-03-18
  • Established by:
  • Revised: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 24, 2019
  • Entry requirements: 120 credits including Data Mining I and Database Design II.
    English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
  • Responsible department: Department of Information Technology

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).

Reading list

Reading list

Applies from: week 24, 2019

  • Tan, Pang-Ning; Steinbach, Michael; Kumar, Vipir Introduction to Data Mining

    1st or international edition: Addison-Wesley, 2006

    Find in the library