This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.
The course will cover the following main topics:
- The data mining process
- Cluster analysis
- Data pre-processing
- Pattern and association mining
- Classification and prediction
Application examples will be given from domains including demographics, image processing and healthcare.
Intended learning outcomes
After the course, the student should be able to:
- Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
- Implement basic pre-processing, association mining, classification and clustering algorithms.
- Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
- Work efficiently in groups and evaluate the algorithms on real-world problems.
Ordinary examExam type:
D: Submission of written work with following oral, external (7-trinsskala)
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted. The group has a shared responsibility for the content of the report.
Mixed exam 1
The students make a joint presentation followed by a group dialogue. Subsequently the students are having individual examination with presentation and / or dialogue with the supervisor and external examiner while the rest of the group is outside the room.
Duration: 20 minutes per student.
The oral exam will cover the entire course's material. The written hand-in is the result of a self-selected data mining project performed in a group of three. The written submission will allow for an allocation of effort in the group.