Universität Wien

052311 VU Data Mining (2018W)

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

  • Tuesday 02.10. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 04.10. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 09.10. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 11.10. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 16.10. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 18.10. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 23.10. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 25.10. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 30.10. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 06.11. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 08.11. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 13.11. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 15.11. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 20.11. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 22.11. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 27.11. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 29.11. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 04.12. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 06.12. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 11.12. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 13.12. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 08.01. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 10.01. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 15.01. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 17.01. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 22.01. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 24.01. 08:00 - 09:30 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 29.01. 16:45 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 31.01. 08:00 - 09:30 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 (KDD).

Subject specific goals:
- Analysis and interpretation of scientific data
- Evaluate results of the analysis process
- Implementation of scalable solutions for huge amounts of data
- Users support and advice

Generic goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in Data Mining and other disciplines

Assessment and permitted materials

Active participation
Work on exercise-sheets
Work on programming assignments in groups
Peer-review of other participants
Final exam

Minimum requirements and assessment criteria

Mandatory prerequisite for this class is the successful completion of FDA or an equivalent lecture.
FDA: 052300 VU Foundations of Data Analysis

20% Exercise-sheets
40% Programming exercises in Team, peer-review
40% Final exam
Attendence is mandatory

>87,00 % 1
between 75,00 % and 86,99 %: 2
between 63,00 % and 74,99 %: 3
between 50,00 % and 62,99 %: 4
< 50% : 5

Examination topics

- Feature selection
- Dimensionality reduction
- Clustering of High Dimensional Data (Subspace Clustering, ...)
- Similarity learning,
- Large Object Cardinalities
- Distributed and Parallel Data Mining
- Privacy Preserving Data Mining
- Sampling and Summarization
- Similarity learning
- Micro-Clustering
- Datastreams (Clustering/Classification)
- Ensables and Multiview-Learning

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

Module: DM

Last modified: Mo 07.09.2020 15:30