Advanced Data Mining, 6 credits

Avancerad Data Mining, 6 hp


Main field of study


Course level

Second cycle

Course type

Single subject and programme course


Patrick Lambrix

Course coordinator

Patrick Lambrix

Director of studies or equivalent

Patrick Lambrix
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Timetable module Language Campus ECV
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 2 (Spring 2021) 202103-202112 3 English Linköping, Valla C
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2021) 202103-202112 3 English Linköping, Valla C

Main field of study


Course level

Second cycle

Advancement level


Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

A bachelor’s degree in one of the following subjects: statistics, mathematics, applied mathematics, computer science, engineering, or equivalent. Completed courses in calculus, linear algebra, statistics and programming are required. 
Documented knowledge of English equivalent to Engelska B/Engelska 6 

Intended learning outcomes

After completion of the course, the student should on an advanced level be able to:

  • use the terminology used in unsupervised learning
  • account for applications in which clustering and association analysis are relevant
  • account for the algorithms used in clustering and association analysis
  • use cluster analysis in order to perform outlier analysis
  • utilize an appropriate software for cluster and association analysis and interpret the obtained outcome

Course content

Association analysis: concepts and methods related to frequent item sets and association rules such as Apriori principle, FP-growth, evaluation of association rules. 

Clustering: concepts and methods related to partitional clustering methods, hierarchical clustering methods, density-based clustering methods, cluster evaluation, outlier analysis.

Teaching and working methods

The teaching comprises lectures, seminars and computer laboratory. Homework and independent study are a necessary complement to the course. Language of instruction: English.


The course is examined by an individual written exam and laboratory assignments.
Detailed information about the examination can be found in the course’s study guide. 

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.


Institutionen för datavetenskap
Code Name Scope Grading scale
TEN1 Written examination 4 credits EC
LAB2 Laboratory work 2 credits EC
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