Data Mining (guided self study)
|Thu 10-12||B222||Hannu Toivonen||21.01.2016-03.03.2016|
|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
- carrying out weekly individual assignments and keeping a learning journal,
- participating in group work where the groups determine research questions and infer knowledge from a larger data set, and
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 http://www.cs.helsinki.fi/en/exams 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:
- Book home page
- Electronic copy of Chapter 6 (from the book home page)
- Additional material on frequent pattern generation
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).
- 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
- Week 2 -- on 28.1.2016 (solutions)
- Week 3 -- on 4.2.2016 (solutions)
- Week 4 -- on 11.2.2016 (solutions)
- Week 5 -- on 18.2.2016 (solutions)
- Week 6 -- on 25.2.2016 (solutions)
Can be found here. Note! Additional details about the group work (esp. peer review) have been added.
Discussion and questions: