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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.

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Headings with content from the Course syllabus ID2211 (Spring 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Basic definitions of graph theory, strong and weak ties, degree distributions and clustering measures.
  • Erdos-Renyi, Wats-Strogatz, configuration model, the small-world effect.
  • Random walks on graphs, Page Rank.
  • Clustering and community detection.
  • Label Propagation, link prediction.
  • Distributional semantics, topic modelling, document summarisation.

Intended learning outcomes

After passing the course, the student shall be able to

  • explain different fundamental concepts of data mining including information network analysis and mining (e.g., basic concepts of graph theory, network models, algorithms for clustering, community detection, label propagation, link prediction etc.)
  • analyse, select, use, and evaluate data mining techniques and algorithms that are based on the above concepts, as well as independently explore existing data mining algorithms and implement them
  • communicate findings, results and ideas in a clear, formal way.

Course disposition

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Literature and preparations

Specific prerequisites

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Recommended prerequisites

Familiarity with the basic probability theory, linear algebra as well as ability to write a non-trivial computer program.


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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


  • 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.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Profile picture Sarunas Girdzijauskas

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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web ID2211

Offered by

EECS/Computer Science

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: