TDDD41 · 6 credits · Advanced level (A1N) · Informationsteknologi, Datateknik, Datavetenskap
Data Mining - Clustering and Association Analysis
Active · next start VT 2027
73%
passed the latest ordinary exam
TEN1 · 27 Mar 2026 · 44 students
81%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 218 students
76%
Harder than 76% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 85 students.
Part of 11 programmes
- Master of Science in Industrial Engineering and Management - International
- Master of Science in Industrial Engineering and Management
- Master of Science in Computer Science and Engineering
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Applied Physics and Electrical Engineering
- Master's Programme in Cybersecurity
- Master's Programme in Data Science and Information Engineering
- Master's Programme in Computer Science
- Master's Programme in Mathematics
Exam
TEN1 Written examination
- 5
- 4
- 3
- U
Ordinary exams
81% passed · 218 students
- 73%44
- 80%41
- 87%45
- 80%41
- 83%47
Re-exams
77% passed · 47 students
- 77%13
- 70%10
- 83%6
- 80%5
- 57%7
- 100%6
- Pass the re-exam: 77% (47 students), compared with 81% (218 students) on the ordinary exam
- Get 4 or 5: 52% (265 students)
Show small sittings (4)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 50%4
- 100%3
- 100%2
- 0%2
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory work
- G
Main rounds
100% passed · 234 students
- 100%51
- 100%42
- 100%44
- 100%47
- 100%50
Later rounds
No sittings with at least 5 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%1
- 100%1
About the course
Aim
The course lays the foundation for professional work and research in which large amounts of data are explored, modified, modelled and assessed to uncover previously unknown patterns and trends. The course focuses on clustering and association analysis. Having completed the course, the student should be able to:
- understand and be able to use important terminology in data mining
- understand and use the theory behind clustering and association analysis
- use knowledge about techniques for clustering and association analysis
- demonstrate insightful assessment of the quality of given data sets and the information content on which clustering and association analysis can be based
- use and evaluate tools for clustering and association analysis
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 clustering, partitional clustering methods, hierarchical clustering methods, density-based clustering methods, cluster evaluation
- Examiner
- Patrick Lambrix
- Scheduled hours
- 26 h
- Main field of study
- Informationsteknologi, Datateknik, Datavetenskap