Visual Analytics

a Daniel Keim et al.: Mastering the information age solving problems with visual analytics. Eurographics Association, 2010.
b Patrick Fiaux et al.: Bixplorer: Visual Analytics with Biclusters. Computer 46 (8) pp. 90-94, 2013.
c Emmanuel Müller et al.: Discovering multiple clustering solutions: Grouping objects in different views of the data. IEEE International Conference on Data Engineering (ICDE), 2012.
d Michael Hund et al.: Visual Quality Assessment of Subspace Clustering. KDD Workshop on Interactive Data Exploration and Analytics (IDEA), 2016.

This lecture teaches how to analyze large, high-dimensional, partially unreliable, and incomplete data using data analysis techniques and interactive visualizations that are tightly coupled. It explains the properties and parameters of important data analysis methods and shows how these methods can be integrated into Visual Analytics systems.

The interdisciplinary character of the development and use of Visual Analytics approaches is emphasized. This also includes questions of visual perception and cognitive processing of visual data and their role in decision-making processes. Special attention is given to the knowledge generation process, the process by which observations, hypotheses, statistical results and other artifacts are generated and managed. The application examples range from financial data (stock prices), data of credit card movements, gene expression data to epidemiological data and patient data. Target groups of such applications are investors, security departments, biologists, statisticians and physicians.

You can see an interview with Prof. Preim on the topic of Visual Analytics on Youtube.

Organizational Issues

Audience: WPF CV-Master 1-3; WPF INF-Master 1-3; WPF IngIF-Master 1-3; WPF WIF-Master 1-3; WPF DKE-Master 1-3; WPF DigiEng-Master 1-3
Graduation: Examination(orally)
ECTS-Credits: 6
Examination requirements:
– Timely registration (approx. four weeks in advance!)
Examination:
– The examination will be a written offline exam
– Date: 27.07.21
– Time: 8:00 – 10:00 am
– Place: Messehalle 2
– For the most current information, check the file “Prüfungsplan” on the corresponding examination office page

– If you cannot take the exam this semester, you will, unfortunately, have to switch to one of the following examination dates in the upcoming winter or summer semester.

Exam

You can find a list of example questions for the exam in the Visual Analytics Example Exam Questions.

Lecture

Due to the current corona situation, lectures and exercises will be held virtually until further notice. The videos for the lecture will be linked on this page together with the corresponding slides. There will be 21 video lectures with a duration of about 60 minutes (corresponding to 14 full lectures with 90 minutes) where you see the slides, a small version of the speaker and the related audio. These lectures are meant to be used at any time or location as it is convenient for you.
Information for upcoming Zoom calls regarding the lecture and exercise will be posted here on the website.

Location: G29-307
Time: Di., 13:00 bis 15:00 (weekly) (→ see LSF)
1. Video Conference (Zoom): 06.04.2021

Join Zoom Meeting:
https://ovgu.zoom.us/j/99536828556
Meeting-ID: 995 3682 8556

Course of Lectures and Slides

#DateContentVideo
108.04. IntroductionPart 1 | Part 2
215.04. ClusteringPart 1 | Part 2
327.04. Subspace ClusteringPart 1 | Part 2
404.05. Cluster Analysis: Validation, Visualization, Outlier DetectionPart 1 | Part 2
511.05. Visual analysis of BiclustersPart 1 | Part 2
625.05. Scatterplot-Based Visual RepresentationsPart 1 | Part 2 | Part 3
708.06. Linear Dimension ReductionVideo
815.06. Non-Linear Dimension ReductionPart 1 | Part 2
922.06. Decision TreesPart 1 | Part 2
1006.07.
Regression Models


Video

Literature & Links

Exercise

Similar to the lecture, the exercise will be performed virtually.

From 27.04. onwards, a new exercise sheet will be put online here every Monday. In the following week we will provide sample solutions for the current exercise sheet. There will be no voting for the exercises this semester. This means that the exercises are voluntary this semester. With regard to the exam, however, we advise everyone to deal with the exercises in detail every week.

For asking and answering questions about the lectures and exercises please use Moodle.

You can login with your student account at the university. There you will find a forum for each exercise sheet and lecture. Write your questions about the corresponding exercises/lectures and we will answer them. After providing the respective sample solution, you have 3 days to post questions about this exercise sheet in the corresponding forum. Afterwards, this forum will be closed. Please check before you ask a question of whether it is already answered in the forum.

Course of Exercises and Slides

#DateThemaSlides & Exercise SheetsSolutions
106.04.Time for preparation Intro VA & Vis
213.04.Time for preparation Intro to R & RStudio
320.04.Time for preparation Creating Vis. with ggplot2
427.04.1. Exercise sheet Exercise Sheet 1 Solution Exercise Sheet 1
504.05.2. Exercise sheet Exercise Sheet 2 Solution Exercise Sheet 2
611.05.3. Exercise sheet Exercise Sheet 3 Solution Exercise Sheet 3
718.05.4. Exercise sheet Exercise Sheet 4 Solution Exercise Sheet 4
825.05.5. Exercise sheet Exercise Sheet 5 Solution Exercise Sheet 5
901.06.6. Exercise sheet Exercise Sheet 6 Solution Exercise Sheet 6
1008.06.7. Exercise sheet Exercise Sheet 7 Solution Exercise Sheet 7
1115.06.8. Exercise sheet Exercise Sheet 8 Solution Exercise Sheet 8
1222.06.9. Exercise sheet Exercise Sheet 9 Solution Exercise Sheet 9
1329.06.10. Exercise sheet Exercise Sheet 10 Solution Exercise Sheet 10
1406.07.11. Exercise sheet Exercise Sheet 11 Solution Exercise Sheet 11