Jove
Visualize
Contact Us

Related Concept Videos

Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...

You might also read

Related Articles

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

Sort by
Same author

Quality of Digital Health Interventions Across Different Health Care Domains: Secondary Data Analysis Study.

JMIR mHealth and uHealth·2023
Same author

System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis.

JMIR mHealth and uHealth·2022
Same author

Space, time and memory: tales from a long road.

Cognitive processing·2018
Same author

The Role of Uncertainty, Awareness, and Trust in Visual Analytics.

IEEE transactions on visualization and computer graphics·2015
Same author

Knowledge Generation Model for Visual Analytics.

IEEE transactions on visualization and computer graphics·2015
Same author

Enabling automatic clutter reduction in parallel coordinate plots.

IEEE transactions on visualization and computer graphics·2006
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 Video

Updated: Jul 10, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

A taxonomy of clutter reduction for information visualisation.

Geoffrey Ellis1, Alan Dix

  • 1Lancaster University. g.ellis@comp.lancs.ac.uk

IEEE Transactions on Visualization and Computer Graphics
|October 31, 2007
PubMed
Summary

Information visualization helps understand large, multivariate datasets. This study classifies clutter reduction techniques, aiding in selecting optimal methods for data visualization challenges.

More Related Videos

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Related Experiment Videos

Last Updated: Jul 10, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

  • Computer Science
  • Data Science
  • Human-Computer Interaction

Background:

  • Large, multivariate datasets are common in data science.
  • Information visualization aids in understanding complex data.
  • Overcrowded displays are a significant challenge in visualization.

Purpose of the Study:

  • To analyze and classify clutter reduction methods in information visualization.
  • To create a taxonomy of clutter reduction techniques.
  • To guide the selection and development of visualization strategies.

Main Methods:

  • Analysis of numerous clutter reduction methods.
  • Classification based on clutter reduction approach.
  • Evaluation of benefits and drawbacks of each technique.

Main Results:

  • A comprehensive taxonomy of clutter reduction techniques.
  • Categorization based on how clutter is managed.
  • Identification of trade-offs associated with different methods.

Conclusions:

  • The developed taxonomy serves as a guide for matching techniques to specific visualization problems.
  • It facilitates the critique and enhancement of existing methods.
  • Aids in the development of novel clutter reduction strategies for large datasets.