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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: May 6, 2026

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

Gene expression data clustering using a multiobjective symmetry based clustering technique.

Sriparna Saha¹, Asif Ekbal, Kshitija Gupta

¹Department of Computer Science and Engineering, Indian Institute of Technology, Patna, India.

Computers in Biology and Medicine

|November 12, 2013

Summary

This summary is machine-generated.

A new fuzzy clustering technique effectively groups co-expressed genes from microarray data. This method improves upon existing techniques for biological and biomedical research, aiding in the identification of gene expression patterns.

Keywords:

Archived multiobjective simulated annealing based technique (AMOSA)Automatic determination of number of clusters Clustering Gene expression data clustering Microarray data Multiobjective optimization (MOO)Symmetry

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Published on: July 29, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray technology has revolutionized biological and biomedical research.
Clustering algorithms are crucial for analyzing microarray data and identifying co-expressed genes.
Identifying groups of co-expressed genes in large datasets is a significant challenge.

Purpose of the Study:

To address the challenge of clustering microarray data by framing it as a multiobjective clustering problem.
To develop and evaluate a novel symmetry-based fuzzy clustering technique for gene expression analysis.

Main Methods:

A new symmetry-based fuzzy clustering algorithm was developed.
The proposed technique was applied to five publicly available benchmark microarray datasets.
Performance was evaluated by comparing results with established microarray clustering methods.

Main Results:

The proposed symmetry-based fuzzy clustering technique demonstrated effectiveness on benchmark datasets.
The new method showed competitive or improved performance compared to widely used techniques.
Statistical and biological significance tests confirmed the validity of the identified gene clusters.

Conclusions:

The developed symmetry-based fuzzy clustering technique offers a robust approach for analyzing microarray data.
This method aids in the accurate identification of co-expressed genes, advancing biological and biomedical research.
The findings suggest potential for improved understanding of gene functions and disease mechanisms.