052311 VU Data Mining (2016W)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 19.09.2016 09:00 to Su 25.09.2016 23:59
- Deregistration possible until Su 16.10.2016 23:59
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
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