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What is cluster sampling'? 

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Selection (genetic algorithm)
Sampling (statistics)
Potts model
Cluster (spacecraft)
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Cluster sampling is a statistical technique used across various fields for efficient data collection and analysis. It involves dividing the population into separate groups, or clusters, and then randomly selecting some of these clusters for further study. This method is particularly useful when dealing with large populations where individual sampling might be impractical or too costly. The essence of cluster sampling lies in its ability to manage and analyze data by grouping similar elements, thereby reducing variance and improving the representativeness of the sample. For instance, in graph neural networks (GNNs), cluster-based sampling has been proposed to mitigate the over-smoothing issue by assigning nodes to specific regions of the embedding space, which enhances the nodes' expressivity by ensuring that information is propagated more effectively to a node’s neighbors . Similarly, in stratified sampling, which shares similarities with cluster sampling, the population is divided into strata to minimize variance, with techniques like K-mean cluster analysis being employed to achieve minimum variance between strata. Adaptive sampling, another variant, adjusts the selection of units based on information collected from already selected units, which can be seen as a dynamic form of cluster sampling where the clusters are defined based on the data collected during the sampling process. The Swendsen-Wang algorithm, initially designed for sampling in statistical physics models, also utilizes a form of cluster sampling by automatically connecting adjacent nodes that share similar characteristics, thus forming clusters. In healthcare, cluster sampling has been adapted to address the complexity of health systems, allowing for the sampling of organizations within health systems in a manner that ensures a variety of organization types are included in the sample. The concept of Data shaping Using Cluster Sampling (DUCS) demonstrates the application of cluster sampling in reducing data redundancy and improving model performance by clustering a dataset and extracting a small number of frames from each cluster. Adaptive cluster sampling (ACS) further refines this approach for surveys where the characteristic of interest is sparsely distributed but highly aggregated, by selecting initial samples through simple random sampling and then adapting nearby units based on a pre-specified condition. A clustering sampling method based on a Kohonen neural network illustrates the use of cluster sampling in ensuring a diverse sample by giving different sample quantities to each class based on their characteristics. Lastly, the cluster sampling algorithm has been developed for sequential data assimilation in non-Gaussian and nonlinear settings, showing its applicability in a wide spectrum of problems including medical image retrieval. In summary, cluster sampling is a versatile and efficient sampling method that groups the population into clusters to improve the accuracy and efficiency of data collection and analysis across various applications and fields.

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Papers (5)Insight
Book Chapter•DOI
Two-Stage Adaptive Cluster Sampling
Raosaheb Latpate, Jayant Kshirsagar, Vinod Kumar Gupta, Girish Chandra 
01 Jan 2021
Cluster sampling is a method where units are grouped into clusters, and a random sample of clusters is selected for data collection, as discussed in the paper on adaptive cluster sampling.
Open access•Journal Article•DOI
Stratified sampling using cluster analysis
Norhayati Haron.
01 Jan 2022-Nucleation and Atmospheric Aerosols
Cluster sampling is a method where the population is divided into clusters, and a random sample of clusters is selected for analysis, commonly used in K-mean cluster analysis for data grouping.
Proceedings Article•DOI
Effect of Cluster-based Sampling on the Over-smoothing Issue in Graph Neural Network
Thi Linh Hoang, Viet-Cuong Ta 
19 Oct 2022
Cluster sampling is a method used in the paper to address over-smoothing in Graph Neural Networks by assigning nodes to specific regions in the embedding space to improve expressivity.
Proceedings Article•DOI
Effect of Cluster-based Sampling on the Over-smoothing Issue in Graph Neural Network
19 Oct 2022
Cluster sampling is a method used in the paper to address over-smoothing in Graph Neural Networks by assigning nodes to specific regions in the embedding space to improve expressivity.
Book Chapter•DOI
Cluster Sampling Methods
Adrian Barbu, Song-Chun Zhu 
01 Jan 2020
2 Citations
Cluster sampling involves sampling nodes with labels from a Bernoulli random variable and connecting adjacent nodes with label 1, forming random clusters in a percolation model.

Related Questions

What is the Sampling?4 answersSampling is a statistical method used to select a subset or sample from a population for the purpose of making observations and drawing inferences about the entire population. It is a way to study a representative group of individuals when studying the entire population is not feasible. The sample should be a true representation of the population to ensure that the inferences made from the analysis can be applied to the population. Sampling techniques eliminate bias in choosing the subset. Sampling is integral to research and has implications on the quality of research findings. In quantitative research, sampling focuses on maximizing the statistical representativeness of a population, while in qualitative research, it focuses on complete representation of a phenomenon of interest. Probability sampling, where all individuals have an equal chance of being selected, is ideal but often not practical. Non-probability sampling, such as convenience sampling, is more common. Careful planning and consideration of sample size are important in both types of research. Sampling involves selecting a subset of the population for study due to time, cost, and other constraints. It allows for accurate and reliable information while reducing time and cost. However, there are disadvantages such as inadequate samples, bias, accuracy problems, and sampling errors. Different sampling techniques, such as non-probability and probability sampling, are used to make the samples representative of the population. Sampling is the selection of a subset of the population of interest in a research study. It is a practical way to collect data faster and at a lower cost than attempting to reach every member of the population. Understanding the means by which the data arrived in the database is important for analyzing and drawing conclusions from the data. A sampling device is a tool that includes update circuits and a selection circuit to select a set of values from a plurality of state variables. The update circuits calculate change values and update the values of the state variables and local fields. The selection circuit selects a set of values based on score values and outputs the selected set.
What is cluster sampling?5 answersCluster sampling is a method of sampling where the population is divided into groups or clusters, and a subset of clusters is selected for analysis. This approach is useful when the characteristic of interest is sparsely distributed but highly aggregated. In cluster sampling, the initial sample is selected using simple random sampling, and then nearby units that meet certain conditions are added to the final sample. The purpose of cluster sampling is to reduce variance and improve the precision of estimates. It can be used in various fields such as surveys, data clustering, and pose study.
How to use cluster sampling with simple random sampling?5 answersCluster sampling can be used in conjunction with simple random sampling to improve the precision of estimates. In cluster sampling, the population is divided into clusters, and a subset of clusters is selected using simple random sampling. Within the selected clusters, a subset of units is then selected. This two-stage process allows for more efficient sampling when units within clusters are similar. The use of simple random sampling at the cluster level helps ensure that the selected clusters are representative of the population. This combination of cluster sampling and simple random sampling can provide more precise estimates of population parameters.
When is Multi-stage cluster sampling used?5 answersMulti-stage cluster sampling is commonly used in household surveys when there is no sampling frame available or when the population is widely dispersed. It is a complex sampling design that involves selecting primary units (or clusters) at the first stage and then selecting subunits within the selected primary units at the second stage. The selection of first- and second-stage units can be done with equal or unequal probabilities. This sampling design introduces a complex dependence in the selection of final units, making it challenging to prove asymptotic results. However, several methods have been proposed to address this issue, including coupling methods and bootstrap algorithms. These methods aim to estimate variance, bias, stability, and coverage probability in multi-stage cluster sampling designs.
What are the steps involved in cluster sampling for socioeconomic survey in Bangladesh?5 answersCluster sampling for socioeconomic surveys in Bangladesh involves several steps. First, a multistage and geographically clustered sampling technique is used to select households for the survey. This ensures that the sample is representative of the population. Next, important variables are extracted from the raw data and principal component analysis is applied to form a new dataset. This helps in identifying the different economic activities in Bangladesh. The iterative clustering technique is then used to classify the households into different categories based on the new dataset. This categorization reflects the various economic activities in the country. Overall, these steps help in obtaining a representative sample and classifying households based on their socioeconomic characteristics in Bangladesh.
Is cluster sampling good for a study on exercise?5 answersCluster sampling can be beneficial for studies on exercise. It allows researchers to group individuals based on specific characteristics or behaviors, such as exercise behavior or intention to exercise. This can provide a more comprehensive understanding of exercise patterns within different clusters or stages of adherence to exercise. Additionally, cluster sampling can be useful when studying rare populations or when the population of interest is geographically clustered. By sampling clusters instead of individuals, researchers can ensure that an adequate proportion of units with the characteristic of interest are included in the sample. This can improve the accuracy of estimates for population parameters. Overall, cluster sampling can enhance the precision and validity of exercise studies, particularly when studying specific subgroups or rare populations.

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