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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...

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Related Experiment Video

Updated: Jun 22, 2026

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

Feature cluster selection for high-throughput data analysis.

¹Department of Computer Science, Binghamton University, P.O. Box 6000, Binghamton, NY 13902-6000, USA. lyu@cs.binghamton.edu

International Journal of Data Mining and Bioinformatics

|June 13, 2009

Summary

This summary is machine-generated.

This study introduces feature cluster selection to identify key gene groups for microarray classification. The 3M algorithm efficiently finds predictive gene clusters, aiding biomarker discovery.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

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

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Published on: January 16, 2019

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Feature selection is crucial for microarray classification.
Identifying biomarkers from numerous, disparate gene sets is challenging.

Purpose of the Study:

Introduce feature cluster selection for biomarker identification.
Develop an efficient algorithm for selecting coherent and predictive feature clusters.

Main Methods:

Define the theoretical and empirical formulation of feature cluster selection.
Propose and implement the efficient 3M algorithm.

Main Results:

The 3M algorithm successfully selects predictive gene clusters.
Selected gene clusters are statistically significant in microarray data analysis.

Conclusions:

Feature cluster selection is a viable approach for biomarker discovery.
The 3M algorithm offers an efficient solution for identifying significant gene clusters from microarray data.