Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cluster Sampling Method

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...

You might also read

Related Articles

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

Sort by

Same author

Impact of ectopic fat on brain structure and cognitive function: A systematic review and meta-analysis from observational studies.

Frontiers in neuroendocrinology·2023

Same author

Engineering Interfacial Built-in Electric Field in Polymetallic Phosphide Heterostructures for Superior Supercapacitors and Electrocatalytic Hydrogen Evolution.

Small (Weinheim an der Bergstrasse, Germany)·2023

Same author

Remodeling Zinc Deposition via Multisite Zincophilic Chlorophyll for Powerful Aprotic Zinc Batteries.

Nano letters·2023

Same author

Charge Transfer and Level Lifetime in Molecular Photon-Absorption upon the Quantum Impedance Lorentz Oscillator.

ACS omega·2023

Same author

The importance of temperature monitoring in predicting wound healing.

Journal of wound care·2023

Same author

Heterostructure Interface Engineering in CoP/FeP/CeO<sub></sub> with a Tailored d-Band Center for Promising Overall Water Splitting Electrocatalysis.

Inorganic chemistry·2023

Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026

Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026

Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026

Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026

Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026

Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 31, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Fast clustering using adaptive density peak detection.

Xiao-Feng Wang¹, Yifan Xu²

¹1 Department of Quantitative Health Sciences/Biostatistics Section, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.

Statistical Methods in Medical Research

|October 18, 2015

Summary

This summary is machine-generated.

This study introduces adaptive density peak detection for clustering, overcoming parameter selection challenges. The new method offers fast, accurate clustering for big data analysis.

Keywords:

Clustering automatic intrinsic parameter selection density peak fast computation multivariate kernel density estimation

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Related Experiment Videos

Last Updated: Mar 31, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Area of Science:

Computational Biology
Data Science
Statistical Learning

Background:

Clustering methods often suffer from slow convergence, parameter instability, and outlier sensitivity.
Existing density-based clustering algorithms face difficulties in systematic parameter selection.
The truncated counting measure for local density in prior methods complicates optimal parameter estimation.

Purpose of the Study:

To propose a novel clustering procedure addressing limitations of existing methods.
To introduce adaptive density peak detection using nonparametric multivariate kernel estimation.
To develop an automatic cluster centroid selection method via average silhouette index maximization.

Main Methods:

Local density estimation using nonparametric multivariate kernel estimation.
Statistical theoretical justification for model parameter calculation.
Automatic cluster centroid selection by maximizing the average silhouette index.
Development of the ADPclust R package for public use.

Main Results:

The proposed method achieves fast, single-step clustering without iteration.
Demonstrated advantages and flexibility through simulation studies.
Successfully applied to benchmark gene expression datasets.
The method shows potential for big data analysis due to its speed and efficiency.

Conclusions:

The adaptive density peak detection clustering method provides a robust and efficient alternative.
The automatic parameter selection enhances usability and reliability.
The ADPclust R package facilitates broader application in biological and data science research.