Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Parallel sets: interactive exploration and visual analysis of categorical data.

Robert Kosara1, Fabian Bendix, Helwig Hauser

  • 1Computer Science Department, University of North Carolina at Charlotte, NC 28223-0001, USA. rkosara@uncc.edu

IEEE Transactions on Visualization and Computer Graphics
|June 30, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CrossSet: Unveiling the Complex Interplay of Two Set-typed Dimensions in Multivariate Data.

IEEE transactions on visualization and computer graphics·2025
Same author

Business Data Visualization, Beyond the Boring.

IEEE computer graphics and applications·2024
Same author

Interactive Design-of-Experiments: Optimizing a Cooling System.

IEEE transactions on visualization and computer graphics·2024
Same author

Notebooks for Data Analysis and Visualization: Moving Beyond the Data.

IEEE computer graphics and applications·2023
Same author

Interactive Visual Analysis of Structure-borne Noise Data.

IEEE transactions on visualization and computer graphics·2022
Same author

More Than Meets the Eye: A Closer Look at Encodings in Visualization.

IEEE computer graphics and applications·2022
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Parallel Sets offer a novel visualization method for exploring categorical data, showing frequencies and relationships. This interactive approach enables deeper analysis by allowing users to remap data and consider more dimensions.

Area of Science:

  • Computer Science
  • Data Visualization
  • Information Visualization

Background:

  • Categorical data is prevalent in real-world datasets.
  • Existing visualization methods struggle to effectively represent categorical data.
  • There is a need for advanced tools to explore complex categorical datasets.

Purpose of the Study:

  • Introduce Parallel Sets, a new visualization technique for categorical data.
  • Enable interactive exploration and analysis of high-dimensional categorical data.
  • Demonstrate the utility of Parallel Sets with real-world datasets.

Main Methods:

  • Parallel Sets visualizes data frequencies using an axis layout similar to parallel coordinates.
  • Categories are represented by boxes, and relationships by parallelograms.

Related Experiment Videos

  • Interactive features include data remapping and automatic cross-product generation.

    • Main Results:

    • Parallel Sets effectively visualizes relationships and frequencies within categorical data.
    • Interactive remapping allows for the consideration of more data dimensions.
    • The method facilitates the construction of metalevel, semantic data representations.

    Conclusions:

    • Parallel Sets provide a powerful tool for the visualization and interactive exploration of categorical data.
    • The technique enhances data analysis by enabling deeper insights into data relationships.
    • Demonstrated success in analyzing CRM and housing datasets highlights practical applicability.