| TDDD41 |
Data Mining - Clustering and Association Analysis, 6 ECTS credits.
/Data Mining - Clustering and Association Analysis/
For:
CS
D
DAV
IT
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Prel. scheduled
hours: 26
Rec. self-study hours: 134
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Area of Education: Technology
Main field of studies: Computer Science, Computer Engineerring, Information Technology
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Advancement level
(G1, G2, A): A
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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
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
The course requires thorough knowledge in programming, discrete mathematics, data structures and algorithms and databases.
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Organisation:
The teaching comprises lectures and computer laboratory work. Lectures are devoted to theory, concepts and techniques. The techniques are practised in the computer laboratory work.
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Course contents:
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
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Course literature:
Jiawei Han, Micheline Kamber, Data Mining - Concepts and Techniques, 2nd edition, Morgan-Kaufmann, 2006. ISBN: 978-1-55860-901-3
Article collection 2016
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Examination: |
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Written examination
Laboratory work
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4 ECTS
2 ECTS
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Course language is English.
Department offering the course: IDA.
Director of Studies: Patrick Lambrix
Examiner: Patrick Lambrix
Link to the course homepage at the department
Course Syllabus in Swedish
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