- Basic definitions in graph theory, strong and weak bands, grade distribution and clustering measurements.
- Erdos-Renyi, Wats-Strogatz, ccnfiguration models, the effect of a "small world".
- Random walks in graphs, Page Rank.
- Graph clustering, identification of "communities".
- The algorithm "Label Propagation", link prediction.
- Basics of machine learning of graph representations.
ID2211 Data Mining, Basic Course 7.5 credits
In the course, the foundations of data mining is studied, with a special focus on information network analysis and mining.
Information per course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Information for Spring 2026 Start 16 Mar 2026 programme students
- Course location
KTH Campus
- Duration
- 16 Mar 2026 - 1 Jun 2026
- Periods
Spring 2026: P4 (7.5 hp)
- Pace of study
50%
- Application code
60482
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- Open to all master's programmes as long as it can be included in your programme.
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus ID2211 (Spring 2023–)Headings with content from the Course syllabus ID2211 (Spring 2023–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After passing the course, the student shall be able to
- explain different fundamental concepts and algorithms in data mining and basic technologies for analysis and extraction in information networks (for example the fundamental concepts in graph theory, network models, algorithms left graph clustering, identification of "communities", "Label Propagation", link prediction, etc)
- analyse, choose, use, and evaluate technologies for data mining that is based on the above concepts and explore and implement the existing data mining algorithms independently
- communicate findings, results and ideas with clear and formal language.
Literature and preparations
Specific prerequisites
No information inserted
Recommended prerequisites
Familiarity with the basic probability theory, linear algebra as well as ability to write a non-trivial computer program.
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
A, B, C, D, E, FX, F
Examination
- PRO1 - Project, 3.0 credits, grading scale: P, F
- TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
The exam is written.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Supplementary information
In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.