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, DM566, DM583, DM843, DM868, DM870, or DS804).
The course builds on the knowledge acquired in the courses DM555, DM566, DM583, DM843, DM868, DM870, or DS804, 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.
Course introduction
Expected learning outcome
- describe the data mining tasks presented in the course;
- describe the algorithms and methods introduced in the course;
- describe the topics covered in the course using precise mathematical language;
- explain the individual steps in the mathematical derivations presented in the course;
- apply the methods to problems beyond those presented in the course;
- evaluate and reflect on the design choices of data mining methods for high-dimensional data and ensemble methods;
- understand scientific literature on the covered topics and present scientific results in class;
- engage in meaningful discussions about related scientific topics presented by others.
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
Report
EKA
Assessment
Grading
Identification
Language
Examination aids
ECTS value
Additional information
Indicative number of lessons
Teaching Method
Total number of planned lessons: 30
Hereof:
Common lessons in classroom/auditorium: 30
In the lectures, concepts, theories, and models are introduced and put into perspective. In the exercise classes, students train their skills through exercises and dig deeper into the subject matter.
Other planned teaching activities:
- Reading from textbooks and papers.
- Solving homework.
- Applying acquired knowledge in practical projects.
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.