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Visual Analytics and Visual Data Mining - Seminar

High-dimensional datasets with a huge number of datavalues are often generated during common working processes in business and science. The goal of Visual Analytics & Visual Data Mining is to handle these datasets with the aid of the human cognition.

Here, the term handle means to identify major patterns and textures to be able to describe the relationships between the dimensions itself, like correlations, clusters or bifurcations. To do that, the first step is to generate significant (and often ranked) visualizations from the dataset (VDM). The second step is to integrate these visualizations (and the visualization-process) in an interactive system (VA), with that a human is able to interpret the dataset completely.

To sum up, the questions that should be answered are:

  • Which approaches of visualization are appropriate?
  • How does an interactive system should looks like ?


Lecturer: Dirk J. Lehmann, Steffen Oeltze, Prof. Bernhard Preim, Prof. Holger Theisel
Classification: WPF CV;i ab 8, WPF CV;B 5-6, WPF IF;i ab 8, WPF IF;B 5-6, WPF INGIF;i ab 8, WPF IngINF;B 5-6, WPF WIF;i ab 8, WPF WIF;B 5-6
Requirements for attending the course: basic knowledge in visualization, linear algebra, image processing and theory of cognition
Degree: Schein (graded for Bachelor students)
ECTS-Credits (Bachelor): 3
LP (Diplom): 4

Terms and Conditions:

  • compulsory attendance
  • 30 min. presentation + 15 min. discussion
  • exposition (at least 3 pages, at most 5 pages)
  • active participation in discussions


Room: G29-E037 (ground floor)
Time: Wednesday, 13.15 - 14.45
1. Meeting: 14.10.2009

Lecturer: Dirk J. Lehmann Steffen Oeltze
Office: G29-234G29-212
Phone: (0391) 67 18065(0391) 67 12527