Universität Wien

052311 VU Data Mining (2016W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 06.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 13.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 20.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 27.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 03.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 10.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 17.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 24.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 01.12. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 15.12. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 12.01. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 19.01. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 26.01. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

The lecture covers essential topics in Data Mining and Knowledge Discovery Databases: Feature selection, Feature reduction, Metric learning, Subspace Clustering, Sampling and Micro-Clustering, Stream clustering/ classification, Parallel Data Mining, Distributed Mining and Privacy.

Assessment and permitted materials

There will be two exams, one in the middle of the semester (exact date to be announced) and one in the last class of the semester, each with a maximum of 30 points. You will be able to earn up to 30 points via exercise sheets (homework) and up to 10 points for your active attendence.

Minimum requirements and assessment criteria

Active participation, minimum 50% of the points in exams and exercises

Examination topics

Lectures and exercises (programming and exercise sheets)

Reading list

Han J., Kamber M., Pei J. Data Mining: Concepts and Techniques
Tan P.-N., Steinbach M., Kumar V. Introduction to Data Mining
Ester M., Sander J. Knowledge Discovery in Databases: Techniken und Anwendungen

Association in the course directory

Last modified: Mo 07.09.2020 15:30