Data Mining (guided self study)

Algorithms and machine learning
Advanced studies
This course focuses on concepts and methods for frequent pattern discovery, also known as association analysis. This edition of the course is a structured and guided self-study course with weekly tasks and supervision, with mandatory attendance. Prerequisites: BSc degree and the course Introduction to Machine Learning or equivalent. Course book: Tan P., Steinbach M. & Kumar V.: Introduction to Data Mining, Chapters 6 and 7. Addison Wesley, 2006.
Year Semester Date Period Language In charge
2016 spring 21.01-03.03. 3-3 English Hannu Toivonen


Time Room Lecturer Date
Thu 10-12 B222 Hannu Toivonen 21.01.2016-03.03.2016

Exercise groups

Group: 1
Time Room Instructor Date Observe
Thu 12-14 B221 Arto Vihavainen 21.01.2016—03.03.2016

Information for international students

The course will be given in English.


This course will familiarize the participants with concepts and methods for identifying interesting patterns from large datasets. Data mining is about trying to make sense of data, usually without clear questions or clear success criteria. The course will focus on discovery of frequent patters in data, a fundamental data mining task that can help extract knowledge and previously unknown patterns also from largely unstructured data.

For unofficial IRC guidance, a channel #dm2016 will be set up on IRCNet if needed.

Note that the lab/exercise sessions start already after the first lecture on Thu 21 Jan!

Completing the course

Albeit being a self-study course, the course will contain scheduled activities that are to be completed within a given time-frame. The course is completed by

  1. carrying out weekly individual assignments and keeping a learning journal,
  2. participating in group work where the groups determine research questions and infer knowledge from a larger data set, and
  3. studying.

The Thursday sessions 10-14 are mandatory and start on 21.1.

There are no traditional lectures per se, and as such the learning approach taken in the course is self and group study.

If you wish to take the course without participating in any of the activities, attend a separate exam. See for the exam schedule.

Literature and material

Course book: Tan P., Steinbach M. & Kumar V.: Introduction to Data Mining, Chapters 6 and 7. Addison Wesley, 2006. Links:

Provisional list of material covered (also in separate exams): Chapters 6 and 7 of Tan et al, except sections 6.2.4 (Support Counting), 6.3.2 (Rule Generation in Apriori Algorithm), 6.8 (Effect of Skewed Support Distribution), 7.5 (Subgraph Patterns), 7.6 (Infrequent Patterns).

Individual assignments

  • Set 1 -- due 28.1.2016 10AM
  • Set 2 -- due 4.2.2016 10AM
  • Set 3 -- due 11.2.2016 10AM
  • Set 4 -- due 18.2.2016 10 AM
  • Set 5 -- due 25.2.2016 10AM
  • Set 6 -- due 3.3.2016 10 AM

Weekly tests

Course guidelines:

Can be found here. Note! Additional details about the group work (esp. peer review) have been added.

Discussion and questions:

You can ask questions about the assignments in Piazza. The sign-up link is and the actual forum link is