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.

Summary:

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.


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: 5-6

Examination requirements:

  • At least 2/3 presence in the exercises
  • At least 2/3 of all points in the exercises
  • Presentation of at least 2 homework assignments in the exercises
  • Timely registration (approx. four weeks in advance!)

For the participation in the exercise a registration is necessary until 05.04.2019: Registration

Lecture

Location: G29-E037
Time: Do., 09:00 bis 11:00 (weekly) (→ see LSF)
1. Lecture: 04.04.2019

Lecturer: Prof. Bernhard Preim
Office: G29-211
Tel.: (0391) 67 5 85 12
E-Mail: preim@isg.cs.uni-magdeburg.de

Ablauf und Folien:

Exercise

Exercise:
Location: G29-E037
Time: Fr., 13:00 bis 15:00
1. Exercise: 12.04.2019

Supervisor: Monique Meuschke
Office: G29-207
Tel.: (0391) 67 51 431
E-Mail: meuschke@isg.cs.uni-magdeburg.de

(Expected ) course of events:

# Date Thema Material
1 04.04. Lecture 1
2 05.04. Lecture 2
3 11.04. No Exercise and No Lecture
4 12.04. Exercise: Control exercise sheet 1
5 18.04. Lecture 3
6 19.04. No Exercise(public holiday)
7 25.04. Lecture 4
8 26.04. Lecture 5
9 02.05. Exercise: Introduction to R & RStudio
10 03.05. No Exercise and No Lecture
11 09.05. Lecture 6
12 10.05. Exercise: Exploratory Data Analysis
13 16.05. Exercise: Control exercise sheet 2
14 17.05. Exercise: Control exercise sheet 3
15 23.05. Lecture 7
16 24.05. Exercise: Control exercise sheet 4
17 30.05. No Exercise and No Lecture
18 31.05. Exercise: Control exercise sheet 5
19 06.06. No Exercise and No Lecture
20 07.06. Exercise: Control exercise sheet 6
21 13.06. Lecture 8
22 14.06. Exercise: Control exercise sheet 7
23 20.06. Lecture 9
24 21.06. Exercise: Control exercise sheet 8
25 27.06. Lecture 10
26 28.06. Exercise: Control exercise sheet 9
27 04.07. Lecture 11
28 05.07. Exercise: Control exercise sheet 10
29 11.07. Lecture 12
30 12.07. Exercise: Control exercise sheet 11