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

11.6K
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...
11.6K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

1.8K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
1.8K
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

237
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
237
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

381
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
381
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Robust Adaptation of Foundation Models with Black-Box Visual Prompting.

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

Engineering Amorphous IGZO Thin-Film Transistors: The Role of Composition and Channel Thickness in Mobility-Threshold Voltage Optimization.

ACS omega·2026
Same author

Unicorn: U-Net for sea ice forecasting with convolutional neural ordinary differential equations.

Scientific reports·2025
Same author

Theranostic Gold Nanoparticles Encapsulated in a PEGylated Liposome as an Effective Radiosensitizer for Cancer Radiation Therapy.

ACS applied bio materials·2025
Same author

Variational autoencoder for distributional learning via quantile function estimation.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Phonon-Assisted Charge Trapping and Threshold Voltage Modulation in MoS<sub>2</sub> FETs with AlO<sub><i>x</i></sub>N<sub><i>y</i></sub> Overlayers.

ACS applied materials & interfaces·2025
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2025

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.0K

Sparse kernel k-means clustering.

Beomjin Park1, Changyi Park2, Sungchul Hong2

  • 1Department of Information and Statistics, Gyeongsang National University, Jinju, South Korea.

Journal of Applied Statistics
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new embedded variable selection method for kernel k-means clustering. The method effectively identifies nonlinear clusters and selects relevant variables, improving data analysis for complex datasets.

Keywords:
Nonlinear clusteringanalysis of variance kernelsparse learningvariable selection

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

6.9K
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

11.4K

Related Experiment Videos

Last Updated: Jun 2, 2025

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.0K
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

6.9K
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

11.4K

Area of Science:

  • Data Science
  • Machine Learning
  • Statistics

Background:

  • Clustering algorithms group similar data points to reveal underlying structures.
  • Traditional methods like k-means struggle with nonlinear clusters.
  • Irrelevant variables can hinder clustering accuracy.

Purpose of the Study:

  • To propose an embedded variable selection method for kernel k-means clustering.
  • To enhance nonlinear cluster identification in the presence of irrelevant variables.
  • To provide a reliable tool for analyzing complex datasets.

Main Methods:

  • Developed an embedded variable selection technique.
  • Utilized a tensor product space and a general analysis of variance kernel.
  • Focused on kernel k-means for nonlinear clustering.

Main Results:

  • The proposed method demonstrated competitive performance in simulations.
  • Real-world data analysis confirmed the method's effectiveness.
  • Achieved accurate cluster identification and variable selection.

Conclusions:

  • The novel embedded variable selection method enhances kernel k-means clustering.
  • It effectively handles nonlinear structures and irrelevant variables.
  • Offers a valuable approach for gaining insights from complex data.