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

Cluster Sampling Method01:20

Cluster Sampling Method

15.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.6K
The Representativeness Heuristic02:13

The Representativeness Heuristic

17.1K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
17.1K
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

342
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
342
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.6K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.6K
Behrens–Fisher Test00:57

Behrens–Fisher Test

328
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
328
Trait Centrality01:21

Trait Centrality

247
Trait centrality refers to the degree to which a particular characteristic influences the overall impression of an individual. Some traits exert a disproportionately strong impact on perception, shaping how people interpret other attributes of a person. Solomon Asch first systematically studied this phenomenon in 1946.Asch’s Experiment on Trait CentralityAsch's seminal study demonstrated the centrality of certain traits through a controlled experiment. Participants were presented with a...
247

You might also read

Related Articles

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

Sort by
Same author

The role of PCL reconstruction in knees with combined PCL and posterolateral corner deficiency.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2007
Same author

Rupture of the ankle extensor retinaculum in a dancer.

Journal of the Royal Society of Medicine·2001
Same author

Nerve palsy after hip replacement: medico-legal implications.

International orthopaedics·1999
Same author

Iatrogenic accessory nerve injury.

Annals of the Royal College of Surgeons of England·1996
Same author

Dislocation after hemiarthroplasty of the hip: a comparison of the dislocation rate after posterior and lateral approaches to the hip.

Annals of the Royal College of Surgeons of England·1995
Same author

Should gonadotropin-releasing hormone down-regulation therapy be routine in in vitro fertilization?

Fertility and sterility·1994
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

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

Related Experiment Video

Updated: Apr 4, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Comparing Clusterings Using Bertin's Idea.

A Pilhofer1, A Gribov, A Unwin

  • 1University of Augsburg. alexander.pilhoefer@math.uni-augsburg.de

IEEE Transactions on Visualization and Computer Graphics
|September 11, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient algorithm for ordering nominal classification variables, improving data visualization and analysis. The method enhances the interpretability of cluster comparisons, aiding in the discovery of informative joint orders.

More Related Videos

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

3.1K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.7K

Related Experiment Videos

Last Updated: Apr 4, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K
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

3.1K
A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.7K

Area of Science:

  • Data Visualization
  • Classification Analysis
  • Statistical Computing

Background:

  • Visualizing cluster classifications is challenging, with interpretability decreasing as the number of categories and clusterings increase.
  • Existing graphical displays for comparing clusterings include contingency tables, mosaic plots, and parallel coordinates plots.
  • Effective data ordering, as described by Bertin, simplifies the assimilation of complex information.

Purpose of the Study:

  • To present an efficient algorithm for finding informative joint orders of nominal classification variables.
  • To demonstrate how these optimized orderings improve the interpretability of various graphical displays.
  • To introduce a top-down partitioning algorithm for detecting groups of corresponding categories.

Main Methods:

  • Algorithm development based on Bertin's semiology principles and Kendall's tau concepts.
  • Application of optimized orderings to enhance existing visualization techniques for cluster comparison.
  • Implementation of a top-down partitioning algorithm for category grouping.

Main Results:

  • The proposed algorithm efficiently identifies informative joint orders for multiple nominal classification variables.
  • Optimized orderings significantly improve the clarity and interpretability of visual cluster comparisons.
  • The top-down partitioning algorithm effectively detects groups of corresponding categories.

Conclusions:

  • The developed algorithm and ordering methods offer a significant improvement for visualizing and analyzing classification data.
  • These techniques enhance the ability to discover patterns and relationships within clustered datasets.
  • The methods are accessible through the R package extracat for practical application.