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: 6

Examination requirements:

  • Two times missing in the exercise is allowed
  • Ticking at least 3/4 of the exercise tasks
  • Presentation of at least 2 homework assignments in the exercises
  • Timely registration (approx. four weeks in advance!)

Anyone who has a place in the exercise will please come to the first exercise on Friday, 12.04.2019 so that we can finalize the list of participants. Due to the high demand, we have to allocate free places to other students on the waiting list.

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

Course of Lectures and Slides:

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 2ExerciseSheet_1.pdf
3 11.04. No Exercise and No Lecture 01-Intro_VA_&_Vis.pdf
02-Intro_to_R_&_RStudio.pdf
03-Creating_Vis_with_ggplot2.pdf
4 12.04. Exercise: Control exercise sheet 1 ExerciseSheet_2.pdf
ex_02_solution_template.Rmd
EuropeanSoccer.sqlite
5 18.04. Lecture 3
6 19.04. No Exercise(public holiday)
7 25.04. Lecture 4
8 26.04. Lecture 5 ExerciseSheet_3.pdf
ex_03_solution_template.Rmd
9 02.05. Exercise: Control exercise sheet 2
10 03.05. No Exercise and No Lecture
11 09.05. Lecture 6 ExerciseSheet_4.pdf
12 10.05. Exercise: Control exercise sheet 3 ExerciseSheet_5.pdf
ex_05_solution_template.Rmd
ex_05_example_solution.Rmd
13 16.05. No Exercise and No Lecture
14 17.05. Exercise: Control exercise sheets 4 and 5 ExerciseSheet_6.pdf
ex_06_solution_template.Rmd
ex_06_example_solution.pdf
15 23.05. Lecture 7
16 24.05. Exercise: Control exercise sheet 6ex_07_solution_template.Rmd
ex_07_example_solution.pdf
17 30.05. No Exercise and No Lecture
18 31.05. Exercise: Control exercise sheet 7 ExerciseSheet_8.pdf
19 06.06. No Exercise and No Lecture
20 07.06. No Exercise and No Lecture ExerciseSheet_9.pdf
21 13.06. Lecture 8
22 14.06. Exercise: Control exercise sheet 8 and 9 Exercise_Sheet_10.pdf
23 20.06. Exercise: Control exercise sheet 10ex_11_solution_template.pdf
ex_11_solution_template.Rmd
24 21.06. Lecture 9
25 27.06. Lecture 10
26 28.06. Exercise: Control exercise sheet 11
27 04.07. Lecture 11
28 05.07. Exercise: Control exercise sheet 12