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About course

ID2211 Data Mining, Basic Course 7.5 credits

Administer About course

In the course, the foundations of data mining is studied, with a special focus on information network analysis and mining.

Choose semester and 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.

Application

For course offering

Spring 2025 Start 17 Mar 2025 programme students

Application code

60693

Headings with content from the Course syllabus ID2211 (Spring 2023–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • 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.

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.

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

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.

The exam is written.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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

Add-on studies

No information inserted

Supplementary information

In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.