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...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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 organic...
Chromatographic Resolution01:15

Chromatographic Resolution

In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...
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...
Separation of Sister Chromatids02:17

Separation of Sister Chromatids

At the transition from prophase to metaphase, there is a reduction in cohesion along the chromosomal arms, resulting in the resolution of sister chromatids. However, residual cohesin connections remain to hold the sister chromatids together until the transition from metaphase to anaphase. The residual connection prevents any premature separation of sister chromatids, blocking the risks of aneuploidy within the daughter cells.
At the onset of anaphase, separase, a proteolytic enzyme, is...
Separation of Sister Chromatids02:17

Separation of Sister Chromatids

At the transition from prophase to metaphase, there is a reduction in cohesion along the chromosomal arms, resulting in the resolution of sister chromatids. However, residual cohesin connections remain to hold the sister chromatids together until the transition from metaphase to anaphase. The residual connection prevents any premature separation of sister chromatids, blocking the risks of aneuploidy within the daughter cells.
At the onset of anaphase, separase, a proteolytic enzyme, is...

You might also read

Related Articles

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

Sort by
Same author

Improving the solubility of pseudo-hydrophobic Alzheimer's Disease medicinal chemicals through co-crystal formulation.

bioRxiv : the preprint server for biology·2023
Same author

Supplementation with sodium butyrate protects against antibiotic-induced increases in ethanol consumption behavior in mice.

Alcohol (Fayetteville, N.Y.)·2022
Same author

Manipulations of extracellular Loop 2 in α1 GlyR ultra-sensitive ethanol receptors (USERs) enhance receptor sensitivity to isoflurane, ethanol, and lidocaine, but not propofol.

Neuroscience·2015
Same author

The selection of critical subsets for signal, image, and scene matching.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

Plasma Renin Concentration in Human Hypertension-III: Renin in Relation to Complications of Hypertension.

British medical journal·2010
Same author

Plasma Renin Concentration in Human Hypertension. IV: Renin in Relation to Treatment and Prognosis.

British medical journal·2010
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

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

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

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

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

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

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

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

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

A cluster separation measure.

D L Davies1, D W Bouldin

  • 1Department of Electrical Engineering, University of Tennessee, Knoxville, TN 37916; 17 C Downey Drive, Manchester, CT 06040.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

A novel cluster similarity measure is introduced, evaluating data partition appropriateness. This method is independent of cluster number or partitioning technique, guiding clustering algorithms effectively.

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

Related Experiment Videos

Last Updated: May 29, 2026

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

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

Area of Science:

  • Data Science
  • Machine Learning
  • Cluster Analysis

Background:

  • Evaluating the quality of data partitions is crucial in cluster analysis.
  • Existing methods may depend on the number of clusters or the partitioning algorithm used.
  • A robust measure is needed to assess the inherent similarity within clusters.

Purpose of the Study:

  • To introduce a new measure for assessing cluster similarity.
  • To provide a method for inferring the appropriateness of data partitions.
  • To enable comparison of different data divisions and guide clustering algorithms.

Main Methods:

  • Developed a similarity measure based on decreasing data density from a characteristic cluster vector.
  • The measure quantifies the internal consistency and separation of clusters.
  • The approach is designed to be independent of the number of clusters and partitioning method.

Main Results:

  • The proposed measure effectively indicates the similarity of clusters.
  • It allows for the inference of data partition appropriateness.
  • The measure facilitates comparison between different data partitioning strategies.

Conclusions:

  • The novel similarity measure offers a robust way to evaluate cluster quality.
  • It is a versatile tool applicable across various clustering scenarios.
  • The measure can be integrated into algorithms to improve data partitioning outcomes.