732A75 · 6 credits · Advanced level (A1F) · Statistik
Advanced Data Mining
Active · next start VT 2027
81%
passed the latest ordinary exam
TEN1 · 27 Mar 2026 · 27 students
66%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 148 students
Unusually easy in 2025/26
81% passed the ordinary exam in 2025/26 (27 students), against 64% on average in earlier years (121 students).
Part of 1 programme
Exam
TEN1 Written examination
- A
- B
- C
- D
- E
- F
Ordinary exams
66% passed · 148 students
- 81%27
- 87%23
- 58%24
- 63%40
- 50%34
Re-exams
81% passed · 58 students
- 57%7
- 67%6
- 91%11
- 83%6
- 80%10
- 90%10
- 88%8
- Pass the re-exam: 81% (58 students), compared with 66% (148 students) on the ordinary exam
- Get B or A: 25% (206 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%4
- 100%4
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB2 Laboratory work
- P
Main rounds
100% passed · 156 students
- 100%26
- 100%25
- 100%29
- 100%40
- 100%36
Later rounds
No sittings with at least 5 students.
Show small sittings (5)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%4
- 100%1
- 100%2
- 100%2
About the course
Aim
After completion of the course, the student should on an advanced level be able to:
- apply 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
- apply cluster analysis in order to perform outlier analysis
- utilize an appropriate software for cluster and association analysis and interpret the obtained outcome
Content
The course deals with:
Association analysis: concepts and methods related to frequent item sets and association rules such as Apriori principle, FP-growth, evaluation of association rules.
Cluster analysis: concepts and methods related to partitional clustering methods, hierarchical clustering methods, density-based clustering methods, cluster evaluation, outlier analysis.
- Examiner
- Patrick Lambrix
- Main field of study
- Statistik