Presentation of my tool which visualize multivariate datasets using parallel coordinates

1. Ing. Ľuboš Takáč
PhD student
Faculty of Management Science and Informatics
University of Žilina

2. • Visualization
• Parallel Coordinates (PC)
• Large Multivariate Data Sets (LMDS)
• Problem of visualization LMDS
• Solutions
• Developed Software Tool
• Further research and application

3. • One of the best approach for presenting data from PC to
human
• Advantages
• Global view of data (all in one picture)
• Significant features highlighted (and vice versa)
• Fast understanding of data by human
• Purpose
• Understand raw data
• To see some significant characteristics or anomalies which can be
further examined to gain some additional information about raw
data.

4. • Invented by Maurice d’Ocagne, 1885
• Popularized by Alfred Inselberg, 1959
• Easy construction (like ordinary graph)
• Data are represented by polylines
• Suitable for visualizing multivariate data (more than 3
dimension)
• You can see dependecies between variables
• Distribution per variable

5. Main difference between ordinary graph of function and parallel coordinates is in position of axes.

6. Examples of some 2D functions visualized using parallel coordinates by developed software tool.

7. • Collection of data usually presented in tabular form
Multivariate data set of movies with 7 dimensions.

8. • Records overlapping
• by simply painting records should be overlapped, you loose some
information
• Overlapping the same records
• by simply painting you do not see the difference between
overlapping two or hundreds same records
• Too many records to visualize => one big blur
• imagine resolution 1024x768, ten thousand of records uniformly
distributed over axes (height 768 px means about 13 records per
pixel)

9. Problem of overlapping painted records.

10. • Preprocessing data before visualization
• Paint data sophisticated by Alpha Compositing

11. • Computer graphics painting method which use alpha
channel to define each color (alpha channel –
transparency of color)
• If you paint object with non opaque color, the resulting
color depends on background too
http://en.wikipedia.org/wiki/Alpha_compositing

12. Visualizing the same randomly generated multivariate data sets by opaque color (upper image) and
using alpha compositing technique (right image).

13. • Based on mentioned principles
• Interactive analyzing of LMDS
• Interactive set operation (selection, difference,
intersection …)
• High quality, antialiased image
• Data import from text file
• Record count is limited to hundreds thousand at rs.
1920x1080

14. Demonstration of developed software tool. Visualized data sets come from IMDb (Internet Movie Database).

15. • Tool can help decision makers and data analyst to gain
some added information to do better decisions.
• Medical data
• Scholar data

16. lubos.takac@gmail.com