- Introduction to Data Mining
- Frequent Itemsets
- Finding Similar Items
- Clustering
- Recommendation Systems
- Mining Data Streams
- Dimensionality Reduction
- Large-Scale Machine Learning
ID2222 Data Mining 7.5 credits

Information per course offering
Information for Autumn 2025 Start 27 Oct 2025 programme students
- Course location
KTH Campus
- Duration
- 27 Oct 2025 - 12 Jan 2026
- Periods
Autumn 2025: 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
- Part of programme
Master's Programme, Software Engineering of Distributed Systems, year 1, DASC
Master's Programme, Computer Science, year 2, CSDA
Master's Programme, Software Engineering of Distributed Systems, year 1, PVT
Master's Programme, Industrial Engineering and Management, year 1, MAIG
Contact
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–)Content and learning outcomes
Course contents
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
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
Grading scale
Examination
- TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
- LAB1 - Programming Assignments, 3.0 credits, grading scale: P, 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.
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
Offered by
Main field of study
Education cycle
Supplementary information
In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.