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
ECTS-Credits: 6
Examination requirements:
– Timely registration (approx. four weeks in advance!)
– Voting for at least 67% of the exercise tasks

Exam

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

Lecture

We would prefer to hold the lectures and one of the exercise sessions in presence, however, we will adapt to the corona situation at the given time.
We will post the necessary information regarding the lecture and exercise sessions here on the website.

In addition to the synchronous lectures, you can find the lecture video recordings from a previous year below. You may use them as a supplement, they do not introduce information beyond the synchronous lecture.

Location: G29-307
Time: Fr., 09:00 – 11:00 (weekly) (→ see LSF)
1. Lecture (in presence): 12.04.2024

Course of Lectures and Slides

#ContentVideo
1 IntroductionPart 1 | Part 2
2 ClusteringPart 1 | Part 2
3 Subspace ClusteringPart 1 | Part 2
4 Cluster Analysis: Validation, Visualization, Outlier DetectionPart 1 | Part 2
5 Visual analysis of BiclustersPart 1 | Part 2
6 Scatterplot-Based Visual RepresentationsPart 1 | Part 2 | Part 3
7 Linear Dimension ReductionVideo
8 Non-Linear Dimension ReductionPart 1 | Part 2
9 Decision TreesPart 1 | Part 2
10 Regression ModelsVideo
(Optional Presentation) Visual Analytics in HealthcarePart 1 | Part 2 | Part 3
(Optional Presentation) Interactive Cooperative Visual AnalyticsPart 1 | Part 2 | Part 3

Exercise

From 29.04 onwards, a new exercise sheet will be posted online here every Monday. In the following week, you will have to vote for the tasks for which you feel able to present your solution in your corresponding exercise session. At the end of the semester, you must have voted for at least 67% of the exercise tasks to be allowed to take the exam.

You have to sign up for an exercise group until the 19.04. via LSF.

Course of Exercises and Slides

#Presentations in weekTopicSlides & Exercise SheetsSolutions & Materials
108.04.Introduction 1 (no meeting) Intro VA & Vis
215.04.Introduction 2 (no meeting) Intro to R & RStudio Heights_dataset
322.04.Introduction 3 (no meeting) Creating Vis. with ggplot2
429.04.1. Exercise sheet Exercise Sheet 1
506.05.2. Exercise sheet
613.05.3. Exercise sheet
720.05.No Exercise due to Whitsun
827.05.4. Exercise sheet
903.06.5. Exercise sheet
1010.06.6. Exercise sheet
1117.06.7. Exercise sheet
1224.07.8. Exercise sheet
1301.07.9. Exercise sheet
1408.07.10. Exercise sheet