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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...

Sampling Plans

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...

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

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Related Experiment Video

Updated: May 9, 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

A heuristic cluster-based EM algorithm for the planted (l, d) problem.

Yipu Zhang¹, Hongwei Huo, Qiang Yu

¹Department of Computer Science, Xidian University, Xi'an, 710071, Shaanxi, P. R. China. zephyr26026@gmail.com.

Journal of Bioinformatics and Computational Biology

|July 18, 2013

Summary

This summary is machine-generated.

A new cluster-based Expectation-Maximization (EM) algorithm, CEM, improves transcription factor binding site (TFBS) discovery by refining subsets to avoid local optima. This method enhances motif identification accuracy for gene regulatory analysis.

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

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Last Updated: May 9, 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

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Transcription factor binding sites (TFBSs) are essential for gene regulation.
Existing Expectation-Maximization (EM) algorithms struggle with highly degenerate motifs and local optima in TFBS discovery.

Purpose of the Study:

To develop a novel algorithm that overcomes limitations of traditional EM for TFBS identification.
To improve the accuracy and robustness of motif discovery, especially for challenging datasets.

Main Methods:

A heuristic cluster-based Expectation-Maximization (CEM) algorithm was developed.
CEM refines cluster subsets within the EM framework to explore optimal solutions.
The algorithm was tested on both synthetic and real biological datasets.

Main Results:

CEM demonstrated significant improvements in identifying motif instances compared to existing algorithms.
The algorithm showed enhanced performance in finding TFBSs, particularly for degenerate motifs.
CEM proved effective in mitigating the issue of local optima trapping inherent in standard EM.

Conclusions:

CEM offers a novel and effective approach to the planted motif search problem.
The algorithm provides a more robust solution for TFBS discovery, crucial for understanding gene regulation.
CEM's design facilitates parallel processing, enhancing computational efficiency.