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

13.8K
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...
13.8K
Aggregates Classification01:29

Aggregates Classification

892
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
892
Sampling Plans01:23

Sampling Plans

809
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...
809
Stratified Sampling Method01:16

Stratified Sampling Method

14.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
14.4K
RNA-seq03:21

RNA-seq

11.6K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
11.6K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.5K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Research Progress on Avian Influenza Virus and Autophagy: A Review.

Pathogens (Basel, Switzerland)·2026
Same author

From facilitating conditions to intention to use internet hospitals among doctors: multiple mediators of perceived risk and performance expectancy.

BMC health services research·2026
Same author

Patient-reported outcomes in randomized controlled trials of spinal disorders: a methodological quality assessment and recommendations for future research.

EFORT open reviews·2026
Same author

Author Correction: Polymer-mRNA complexes for monocyte-trafficked, lymph node-targeted cancer vaccination.

Nature biomedical engineering·2026
Same author

Polymer-mRNA complexes for monocyte-trafficked, lymph node-targeted cancer vaccination.

Nature biomedical engineering·2026
Same author

Responses of <i>Japonica</i> Rice Quality Indicators and Starch Properties to Low Temperature at Different Periods of the Grain-Filling Stage in Cold Regions.

Foods (Basel, Switzerland)·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Dec 23, 2025

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

11.8K

Self-Paced Clustering Ensemble.

Peng Zhou, Liang Du, Xinwang Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 21, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a self-paced clustering ensemble (SPCE) method. It improves consensus clustering by learning from easy to difficult data instances, enhancing accuracy.

    More Related Videos

    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

    11.4K
    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

    7.2K

    Related Experiment Videos

    Last Updated: Dec 23, 2025

    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

    11.8K
    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

    11.4K
    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

    7.2K

    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Clustering ensembles integrate multiple clusterings for robust results.
    • Existing methods often struggle with difficult data instances, impacting consensus quality.

    Purpose of the Study:

    • To propose a novel self-paced clustering ensemble (SPCE) method.
    • To address the limitations of existing methods by handling difficult instances effectively.

    Main Methods:

    • Developed a self-paced learning approach to gradually include instances based on difficulty.
    • Integrated instance difficulty evaluation and ensemble learning into a unified framework.
    • Proposed a joint learning algorithm for optimizing the objective function.

    Main Results:

    • The proposed SPCE method demonstrates effectiveness on benchmark datasets.
    • The approach automatically estimates instance difficulty and ensembles base clusterings.

    Conclusions:

    • The self-paced clustering ensemble (SPCE) method offers an improved approach to consensus clustering.
    • This method enhances robustness by adaptively managing data instance complexity.