DM583: Data Mining
The Study Board for Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N330071102
Assessment: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Bachelor
STADS ID (UVA): N330071101
ECTS value: 5
Date of Approval: 02-03-2023
Duration: 1 semester
Version: Approved - active
Entry requirements
The course cannot be chosen by students who: have either followed, or have passed DM555, DM566, DM855, DM859, DM868, DM870, or DS804.
Academic preconditions
Students taking the course are recommended to:
- Have knowledge of the basic concepts of discrete methods for computer science
- Have knowledge oft the basic concepts of linear algebra
- Have knowledge of basic algorithms and data structures
- Be able to program
Course introduction
The aim of the course is to enable the student to choose and use techniques from Data Mining, which is important in regard to being able to analyze large datasets in many financial, medical, commercial, and scientific applications.
Data Mining techniques enable computational systems to identify meaningful patterns in the data.
This course introduces the most common techniques for performing basic data mining tasks, and covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. Computational learning methods are introduced at a general level, with their basic ideas and intuition. Moreover, the students have the opportunity to experiment and apply data mining techniques to selected problems.
The course builds on basic programming skills (DM574), data structures and algorithms (DM578) in the design of data mining algorithms, database systems (DM576), and linear algebra (DM579). The unsupervised learning techniques taught in this course complement the supervised learning techniques taught in DM581.
The course gives an academic basis for conducting large scale data analysis and for conducting bachelor and master thesis projects as well as other practical oriented study-activities, that are part of the degree.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- Give knowledge of common data mining tasks and methods
- Give skills to apply common data mining methods to real world problems
- Give the competence to design data mining methods
- Give knowledge to understand and reflect on theories, methods, and practices in the computer science field
- Give skills to acquire new knowledge in an effective and independent manner and be able apply this knowledge in a reflective way
- Give skills to describe, analyze and solve computer science problems applying methods and modeling formalisms from the core area and its mathematical support disciplines
- Give skills in analyzing the advantages and disadvantages of various algorithms, especially in terms of resource consumption
- Give skills to make and justify professional decisions
- Give skills to describe, formulate and communicate issues and results to peers, non-specialists, project partners and users.
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 the mathematical derivations presented in class
- Apply the methods to simple problems
- Apply the methods to situations different from the ones presented in class
- Reflect on and assess design choices for data mining systems
- Undertake experimental evaluation of data mining methods and report the results
Content
The following main topics are contained in the course:
- basic continuous probability
- methods (partitioning clustering, density-based clustering, hierarchical clustering, outlier detection)
- frequent pattern mining (item set mining, association rules)
- evaluation of unsupervised learning
Literature
Examination regulations
Exam element a)
Timing
June
Tests
Written exam
EKA
N330071102
Assessment
Second examiner: External
Grading
7-point grading scale
Identification
Student Identification Card - Exam number
Language
Normally, the same as teaching language
Duration
4 hours
Examination aids
All common aids are allowed e.g. books, notes and computer programmes which do not use internet etc.
internet is not allowed during the exam. However, you may visit system DE-Digital Exam when answering the multiple-choice questions. If you wish to use course materials from itslearning, you must download the materials to your computer the day before the exam. During the exam itslearning is not allowed.
ECTS value
5
Additional information
Re-exam will be held orally if 24 or fewer students register.
Indicative number of lessons
Teaching Method
At the faculty of science, teaching is organized after the three-phase model i.e. intro, training and study phase.
- Intro phase (lectures) - 18 hours
- Training phase: 12 hours, including 12 hours tutorials
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.
Activities during the study phase:
- Reading from textbooks
- Solving homework
- Applying acquired knowledge in practical projects
Teacher responsible
| Name | Department | |
|---|---|---|
| Ricardo Jose Gabrielli Barreto Campello | campello@imada.sdu.dk | Institut for Matematik og Datalogi |
Timetable
Administrative Unit
Team at Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.