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052311 VU Data Mining (2017W)
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 Sa 09.09.2017 09:00 to Su 24.09.2017 23:59
- Deregistration possible until Su 15.10.2017 23:59
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
Assessment and permitted materials
Active participation
Work on exercise-sheets
Work on programming assignments in groups
Peer-review of other participants
Final exam
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 Analysis20% 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
FDA: 052300 VU Foundations of Data Analysis20% 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
- 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
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
- Analysis and interpretation of scientific data
- Evaluate results of the analysis process
- Implementation of scalable solutions for huge amounts of data
- Users support and adviceGeneric goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in Data Mining and other disciplines