Data Mining (guided self study)
5
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 |
|---|---|---|---|---|---|
| 2015 | spring | 11.03-29.04. | 4-4 | English | Hannu Toivonen |
Lectures
| Time | Room | Lecturer | Date |
|---|---|---|---|
| Wed 12-14 | C222 | Hannu Toivonen | 11.03.2015-29.04.2015 |
Exercise groups
| Time | Room | Instructor | Date | Observe |
|---|---|---|---|---|
| Mon 10-12 | B221 | Arto Vihavainen | 13.03.2015—30.04.2015 | |
| Fri 14-16 | B221 | Arto Vihavainen | 13.03.2015—30.04.2015 |
Ilmoittautuminen tälle kurssille alkaa tiistaina 17.2. klo 9.00.
Registration for this course starts on Tuesday 17th of February at 9.00.
General
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 #dm2015 has been set up on IRCNet
Note! Please fill in the course feedback form at https://ilmo.cs.helsinki.fi/kurssit/servlet/Valinta?kieli=en -- when you enter the page, select "Data Mining" from the course list.
After this course, consider taking the Data Mining Project
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
- studying.
The wednesday meetings are mandatory.
There are no traditional lectures per se, and as such the learning approach taken in the course is self and group study. Participants will get guidance during the lab-times which are voluntary.
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
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).
Course guidelines:
Can be found at http://tinyurl.com/dm2015-guidelines
Assignments:
Individual assignments, week 1
Individual assignments, week 2
Individual assignments, week 3
Individual assignments, week 4
Individual assignments, week 5