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

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...
Sampling Plans01:23

Sampling Plans

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...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Distance Problem01:29

Distance Problem

When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
Reduced Mass Coordinates: Isolated Two-body Problem01:12

Reduced Mass Coordinates: Isolated Two-body Problem

In classical mechanics, the two-body problem is one of the fundamental problems describing the motion of two interacting bodies under gravity or any other central force. When considering the motion of two bodies, one of the most important concepts is the reduced mass coordinates, a quantity that allows the two-body problem to be solved like a single-body problem. In these circumstances, it is assumed that a single body with reduced mass revolves around another body fixed in a position with an...
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

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 or...

You might also read

Related Articles

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

Sort by
Same author

Neuropsychological impairments in emotion recognition compared to general cognition: profiles across six different neurological disorders.

Journal of neurology·2026
Same author

External Validation of a Prognostic Model for Outcome After Mild Traumatic Brain Injury at 6 Months Post Injury.

European journal of neurology·2026
Same author

Remembering affect between moments: assessing peak-end effects in continuous affect measures.

Cognition & emotion·2026
Same author

Statistical process control for real-time monitoring in clinical psychology: State of the art and future research agenda.

Neuroscience applied·2026
Same author

Unravelling the small sample bias in AR(1) models: The pros and cons of available bias correction methods.

The British journal of mathematical and statistical psychology·2026
Same author

Detecting association changes in intensive longitudinal data in real time: An exponentially weighted moving average procedure.

The British journal of mathematical and statistical psychology·2026
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: May 13, 2026

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

Subspace K-means clustering.

Marieke E Timmerman1, Eva Ceulemans, Kim De Roover

  • 1Heymans Institute for Psychology, Psychometrics & Statistics, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands, m.e.timmerman@rug.nl.

Behavior Research Methods
|March 26, 2013
PubMed
Summary
This summary is machine-generated.

Subspace K-means offers insightful clustering for multivariate data by modeling centroids and residuals in reduced spaces. This novel approach outperforms existing methods like K-means and Factorial K-means in complex data scenarios.

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

Related Experiment Videos

Last Updated: May 13, 2026

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

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

Area of Science:

  • Data Science
  • Machine Learning
  • Statistical Modeling

Background:

  • Multivariate data clustering is crucial for uncovering hidden patterns.
  • Existing methods like K-means and Factorial K-means have limitations in handling complex cluster structures.
  • Need for robust clustering algorithms that provide interpretable results.

Purpose of the Study:

  • Propose and evaluate subspace K-means, a novel clustering algorithm.
  • Model centroids and residuals in reduced spaces for enhanced interpretability.
  • Compare subspace K-means against established clustering techniques.

Main Methods:

  • Developed the subspace K-means algorithm.
  • Conducted comparative simulation studies with varying subspace overlap, between-cluster variance, and error variance.
  • Applied subspace K-means to real-world parental behavior data.

Main Results:

  • Subspace K-means demonstrates superior performance in recovering true clusters across diverse conditions.
  • The algorithm is sensitive to local minima, but this can be mitigated using robust initialization strategies.
  • Outperforms K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST.

Conclusions:

  • Subspace K-means provides rich insights into cluster characteristics, including centroid positions and residual shapes.
  • The method is effective for a wide range of cluster types and offers superior performance over competitors.
  • Offers a valuable tool for insightful multivariate data analysis and interpretation.