DM864: Advanced Data Mining
- Have basic knowledge of probability and mathematics;
- Be able to program;
- Be familiar with basics of unsupervised data mining (e.g. from DM555, DM566, DM583, DM843, DM868, DM870, or DS804).
- Give the competence to independently describe, analyse, and solve advanced problems in unsupervised data mining using the acquired models and methods.
- Give the competence to analyse advantages and drawbacks of different methods for advanced unsupervised data mining.
- Give skills to apply the acquired models and methods adequately.
- Give knowledge and understanding of a selection of specialized models and methods for unsupervised data mining using ensemble techniques or adaptations to high-dimensional data, including some from the research frontier of the field.
Expected learning outcome
- beskrive de data mining opgaver som præsenteres i kurset;
- beskrive de algoritmer og metoder som bliver præsenteret i kurset;
- beskrive de emner der bliver præsenteret i kurset i et præcist matematisk sprog;
- forklare de enkelte trin i de matematiske udledninger der præsenteres i kurset;
- anvende metoderne på andre problemstillinger end dem der bliver præsenteret i kurset;
- vurdere og reflektere over valg af design af data mining metoder for høj dimensional data og ensemble metoder.
- general principles and methods for ensemble learning;
- special challenges and approaches for ensemble clustering and ensemble outlier detection;
- selected methods for ensemble clustering and ensemble outlier detection;
- special challenges for data mining in high-dimensional data;
- general approaches for unsupervised learning in high-dimensional data
- selected methods for subspace clustering;
- selected methods for high-dimensional outlier detection.
Prerequisites for participating in the exam a)
To be announced during the course.
Presentation of one or more scientific articles in class
The prerequisite examination is a prerequisite for participation in exam element a).
Exam element a)
|Type||Prerequisite name||Prerequisite course|
|Examination part||Prerequisites for participating in the exam a)||N340084101, DM864: Advanced Data Mining|
Indicative number of lessons
- Intro phase: 22 hours
- Skills training phase: 8 hours, hereof tutorials: 8 hours
- Reading from textbooks and papers.
- Solving homework.
- Applying acquired knowledge in practical projects.
In the intro phase, concepts, theories, and models are introduced and put into perspective. In the training phase, students train their skills through exercises and dig deeper into the subject matter. In the study phase, students gain academic, personal, and social experiences that consolidate and further develop their scientific proficiency. Focus is on immersion, understanding, and development of collaborative skills.