DM864: Advanced Data Mining

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340084112, N340084102
Censorship: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340084101
ECTS value: 5

Date of Approval: 09-03-2020

Duration: 1 semester

Version: Approved - active


15020101(former UVA) is identical with this course description. 

Entry requirements


Academic preconditions

Students taking the course are expected to:

  • 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 aim of the course is to enable the student to understand and work with advanced unsupervised data mining methods such as ensemble methods for clustering and outlier detection or methods dedicated to high-dimensional data (e.g., subspace clustering), which is important in regard to handle complex, difficult, and high-dimensional data in various applications.

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

The learning objectives of the course are that the student demonstrates the ability to:

  • 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.


The following main topics are contained in the course:

  • 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.


See Blackboard for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)




Oral presentation




Second examiner: None




Student Identification Card


Normally, the same as teaching 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)




Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N340084101, DM864: Advanced Data Mining


Oral exam




Second examiner: External


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

A closer description of the exam rules will be posted under 'Course Information' on Blackboard.

ECTS value


Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

36 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 13 hours
  • Skills training phase: 26 hours, hereof tutorials: 11 hours and laboratory exercises: 26 hours
Activities during the study phase:
  • 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.

Teacher responsible

Name E-mail Department
Arthur Zimek Institut for Matematik og Datalogi, Datalogi


08 - 09
09 - 10
10 - 11
11 - 12
12 - 13
13 - 14
14 - 15
15 - 16
Show full time table

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Registration & Legality


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