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

15.1K
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...
15.1K
Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Refining the Definition for "Low Risk" in Pulmonary Arterial Hypertension: Time to Reduce Morbidity and Mortality.

JACC. Heart failure·2026
Same author

Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.

Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference·2026
Same author

Soccer heading and white matter microstructural changes: a two-year longitudinal cohort study.

Brain imaging and behavior·2026
Same author

Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Baseline ECG and Cardiovascular Outcomes in People With HIV: Insights From REPRIEVE.

Journal of the American Heart Association·2025
Same author

An Autoethnography on Visualization Literacy: A Wicked Measurement Problem.

IEEE transactions on visualization and computer graphics·2025

Related Experiment Video

Updated: Feb 23, 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

12.0K

Clustervision: Visual Supervision of Unsupervised Clustering.

Bum Chul Kwon, Ben Eysenbach, Janu Verma

    IEEE Transactions on Visualization and Computer Graphics
    |September 4, 2017
    PubMed
    Summary

    Clustervision is a new visual analytics tool that helps data scientists choose the best clustering algorithms and parameters. It ranks clustering results and allows user-guided exploration for effective data representation.

    More Related Videos

    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
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.7K

    Related Experiment Videos

    Last Updated: Feb 23, 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

    12.0K
    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
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.7K

    Area of Science:

    • Data Science
    • Machine Learning
    • Data Visualization

    Background:

    • Clustering is a key unsupervised machine learning technique for data summarization.
    • A wide array of clustering algorithms exist, making selection and parameterization challenging for data scientists.
    • Identifying optimal clustering for specific datasets and analytical tasks remains a significant hurdle.

    Purpose of the Study:

    • To develop Clustervision, a visual analytics tool to aid data scientists in selecting appropriate clustering algorithms and parameters.
    • To address the difficulty in choosing and configuring clustering methods for complex, multi-dimensional data.
    • To empower users to find, explore, and select high-quality clustering results tailored to their analytical needs.

    Main Methods:

    • Developed Clustervision, a visual analytics system integrating diverse clustering techniques.
    • Implemented a ranking system for clustering results based on five quality metrics.
    • Incorporated user-guided constraints to refine clustering relevance.
    • Utilized coordinated visualization techniques for cluster exploration.

    Main Results:

    • Clustervision effectively assists data scientists in navigating numerous clustering algorithms and parameters.
    • The system successfully ranks clustering outcomes, facilitating the identification of superior results.
    • User-guided constraints enhance the system's ability to produce task-relevant clustering.
    • A medical domain case study demonstrated Clustervision's efficacy in empowering users to select optimal data representations.

    Conclusions:

    • Clustervision provides a robust solution for the challenge of selecting and parameterizing clustering algorithms.
    • The tool enhances the process of discovering meaningful patterns in complex datasets through visual analytics.
    • Clustervision empowers users, particularly in specialized domains like medicine, to achieve effective data representation and analysis.