DM864: Advanced Data Mining
Comment
Entry requirements
Academic preconditions
- Have basic knowledge of probability and mathematics;
- Be able to program;
- Be familiar with basics of unsupervised data mining (e.g. from DM555 or from DM843 – the latter can be combined with this course in the same term).
Course introduction
The course builds on the knowledge acquired in the courses DM555 or DM843, and gives an academic basis for working in applied projects or writing a Master’s thesis in topics involving the unsupervised analysis of complex, difficult, and high-dimensional data.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- 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
- describe the data mining tasks presented during the course;
- describe the algorithms and methods presented in the course;
- describe the topics presented in the course in precise mathematical language;
- explain the individual steps of mathematical derivations presented in class;
- apply the methods to situations different from the ones presented in class;
- reflect on and assess design choices for data mining methods for high-dimensional data and ensemble methods.
Content
- 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.
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
Tests
Oral presentation
EKA
Assessment
Grading
Identification
Language
Examination aids
To be announced during the course.
ECTS value
Additional information
Presentation of one or more scientific articles in class
The prerequisite examination is a prerequisite for participation in exam element a).
Exam element a)
Timing
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Examination part | Prerequisites for participating in the exam a) | N340084101, DM864: Advanced Data Mining |
Tests
Oral exam
EKA
Assessment
Grading
Identification
Language
Examination aids
A closer description of the exam rules will be posted under 'Course Information' on Blackboard.
ECTS value
Additional information
Indicative number of lessons
Teaching Method
- Intro phase: 13 hours
- Skills training phase: 26 hours, hereof tutorials: 11 hours and laboratory exercises: 26 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.