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ID2222 Data Mining 7.5 credits

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.

Termin

Information for Autumn 2025 Start 27 Oct 2025 programme students

Course location

KTH Campus

Duration
27 Oct 2025 - 12 Jan 2026
Periods
P2 (7.5 hp)
Pace of study

50%

Application code

50371

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Min: 25

Target group

Open to all programmes as long as it can be included in your programme.

Planned modular schedule
[object Object]
Schedule
Schedule is not published

Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus ID2222 (Spring 2019–)
Headings with content from the Course syllabus ID2222 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Introduction to Data Mining
  • Frequent Itemsets
  • Finding Similar Items
  • Clustering
  • Recommendation Systems
  • Mining Data Streams
  • Dimensionality Reduction
  • Large-Scale Machine Learning

Intended learning outcomes

The course studies fundamentals of data mining, data stream processing, and machine learning algorithms for analyzing very large amounts of data. We will use big data processing platforms, such as MapReduce, Spark and Apache Flink, for implementing parallel algorithms, as well as computation systems for data stream processing, such as Storm and InfoSphere.

After this course, students will be able to mine different types of data, e.g., high-dimensional data, graph data, and infinite/never-ending data (data streams); as well as to program and build data-mining applications. They are also expected to know how to solve problems in real-world applications, e.g., recommender systems, association rules, link analysis, and duplicate detection. Moreover, they will master various mathematical techniques, e.g., linear algebra, optimization, and dynamic programming.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Knowledge of concepts and terminology associated with statistics, database systems, and machine learning; a course on data structures, algorithms, and discrete mathematics (such as ID1021 Algorithms and Data Structures); a course in software systems, software engineering, and programming languages; a course on processing, storing and analysing massive data sets (such as ID2221 Data-Intensive Computing). 

Literature

The contents of the course are derived from the following two textbooks:

A. Rajaraman and J.  D. Ullman, Mining of massive datasets. Cambridge University Press, 2012 (alternative: J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3-rd Ed., Morgan Kaufmann, 2012)

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

  • LAB1 - Programming Assignments, 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.

Written examination. Laboratory tasks.

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.