Universität Wien FIND
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052311 VU Data Mining (2017W)

Continuous assessment of course work

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Thursday 05.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 12.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 19.10. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 09.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 16.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 23.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 30.11. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 07.12. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 14.12. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 11.01. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 18.01. 08:00 - 11:15 Seminarraum 6, Währinger Straße 29 1.OG
Thursday 25.01. 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 (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: Fr 31.08.2018 08:42