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 Concept Videos

Scatter Plot01:15

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Dimensional Analysis01:23

Dimensional Analysis

Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
Dimensional Analysis03:40

Dimensional Analysis

Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
Dimensional Analysis02:19

Dimensional Analysis

The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
Dimensional Analysis01:27

Dimensional Analysis

Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...

You might also read

Related Articles

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

Sort by
Same author

Reflections on Visualizing the COVID-19 Pandemic for the Public.

IEEE computer graphics and applicationsยท2026
Same author

Ambient Analytics: Calm Technology for Immersive Visualization and Sensemaking.

IEEE computer graphics and applicationsยท2026
Same author

ISilDR: Isometric Seriation-Based Dimensionality Reduction for Visual Cluster Analysis.

IEEE transactions on visualization and computer graphicsยท2026
Same author

A Large-Scale Quantitative Analysis of Avatars in VR and AR.

IEEE transactions on visualization and computer graphicsยท2026
Same author

Situated Brushing and Linking in Virtual and Augmented Reality.

IEEE transactions on visualization and computer graphicsยท2026
Same author

Visualization Tasks for Unlabeled Graphs.

IEEE transactions on visualization and computer graphicsยท2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphicsยท2026
Same journal

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

IEEE transactions on visualization and computer graphicsยท2026
Same journal

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

IEEE transactions on visualization and computer graphicsยท2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphicsยท2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphicsยท2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphicsยท2026
See all related articles

Related Experiment Video

Updated: May 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Empirical guidance on scatterplot and dimension reduction technique choices.

Michael Sedlmair1, Tamara Munzner, Melanie Tory

  • 1University of Vienna.

IEEE Transactions on Visualization and Computer Graphics
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

For high-dimensional data analysis, 2D scatterplots are often sufficient for visualizing cluster separation. If 2D is inadequate, try a different dimension reduction (DR) technique before resorting to 3D scatterplots or SPLOMs.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: May 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Data Visualization
  • High-Dimensional Data Analysis
  • Information Visualization

Background:

  • Analysts use dimension reduction (DR) techniques to visualize high-dimensional data.
  • Common visualization methods include 2D scatterplots, interactive 3D scatterplots, and Scatterplot Matrices (SPLOMs).
  • Guidance is needed for selecting effective visualization techniques for cluster separation analysis.

Purpose of the Study:

  • To provide empirical guidance on choosing visualization techniques for cluster separation in high-dimensional data.
  • To evaluate the effectiveness of 2D scatterplots, interactive 3D scatterplots, and SPLOMs in conjunction with DR techniques.

Main Methods:

  • An empirical data study involving 816 scatterplots from 75 datasets, 4 DR techniques, and 3 visualization types.
  • Two human coders manually scored the separability of color-coded classes in each scatterplot.
  • Quantitative data analyzed using heatmaps, supplemented by qualitative discussion of examples.

Main Results:

  • 2D scatterplots are frequently adequate for visualizing cluster separability.
  • Alternative DR techniques in 2D often improve separation more than SPLOMs or interactive 3D.
  • Interactive 3D plots rarely enhance separation and can decrease usability and class separation.

Conclusions:

  • 2D scatterplots are a reliable baseline for visualizing cluster separation.
  • Prioritize exploring different DR techniques in 2D before considering SPLOMs or interactive 3D.
  • A workflow model is proposed to guide analysts in DR exploration and visualization choices.