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

11.9K
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...
11.9K
Aggregates Classification01:29

Aggregates Classification

326
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...
326
Weighted Mean00:57

Weighted Mean

5.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.2K
Survival Tree01:19

Survival Tree

86
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
86
Classification of Systems-II01:31

Classification of Systems-II

146
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,
146
Classification of Systems-I01:26

Classification of Systems-I

188
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:
188

You might also read

Related Articles

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

Sort by
Same author

Hierarchical feature based dual contrastive multiview clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Unsupervised fine-tuning of vision-language models by fusing classifier tuning and visual prompt tuning.

Neural networks : the official journal of the International Neural Network Society·2026

Related Experiment Video

Updated: Jul 5, 2025

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

7.0K

Toward Balance Deep Semisupervised Clustering.

Yu Duan, Zhoumin Lu, Rong Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |January 12, 2024
    PubMed
    Summary

    This study introduces deep semisupervised balanced clustering, a novel method for partitioning data into equal-sized groups. It enhances clustering performance and balance using neural networks for high-dimensional data.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    556
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.5K

    Related Experiment Videos

    Last Updated: Jul 5, 2025

    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

    7.0K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    556
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.5K

    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Mining

    Background:

    • Balanced clustering aims for equal-sized data partitions, a challenge for traditional methods.
    • Existing approaches often limit performance, especially with high-dimensional data.
    • Neural networks excel with high-dimensional data but struggle with incorporating prior knowledge for balanced clustering.

    Purpose of the Study:

    • To propose a novel deep semisupervised balanced clustering method.
    • To simultaneously learn clustering and generate balance-favorable representations.
    • To overcome limitations of conventional methods in high-dimensional and balanced clustering.

    Main Methods:

    • Developed a deep semisupervised balanced clustering model based on the autoencoder paradigm.
    • Introduced a balance-oriented clustering loss function.
    • Incorporated pairwise constraints using the Lagrangian multiplier method as a pluggable module.

    Main Results:

    • Demonstrated significant improvements in clustering performance across four datasets.
    • Achieved enhanced balanced measurements compared to existing methods.
    • Theoretically ensured balanced orientation and comprehensive optimization.

    Conclusions:

    • The proposed deep semisupervised balanced clustering method effectively addresses limitations of prior work.
    • The model shows superior performance in both clustering accuracy and balance.
    • This approach offers a promising direction for balanced clustering in high-dimensional spaces.