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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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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

Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

Ziyi Song¹, Weining Shen², Marina Vannucci³

¹Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, United States.

|October 30, 2024

Summary

This study introduces a new method for analyzing mouse-tracking data to understand cognitive processes. The hierarchical shrinkage partition (HSP) model effectively clusters individual decision-making behaviors and identifies distinct neurobehavioral subgroups.

Keywords:

Bayesian nonparametrics Dirichlet process clustering nonexchangeable random partitions

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Area of Science:

Neuroscience
Cognitive Science
Data Science

Background:

Mouse-tracking data offer insights into cognitive processes by recording mouse trajectories during tasks.
Clustering these responses helps identify individual decision-making patterns and neurobehavioral subgroups.
Combining mouse-tracking with neuroimaging data can enhance personalized interventions.

Purpose of the Study:

To develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics from mouse-tracking data.
To enable the identification of subject subgroups with similar nested decision-making patterns.
To offer a flexible clustering approach that accommodates variations within subject groups.

Main Methods:

Developed a novel hierarchical shrinkage partition (HSP) prior for clustering mouse-tracking summary statistics.
Defined subject clusters based on similar nested partitions of conditions, allowing for deviations.
Incorporated prior information on subject or condition partitioning to facilitate clustering.

Main Results:

Demonstrated the effectiveness of the HSP model in clustering mouse-tracking data from a pilot study.
Simulation studies confirmed the model's ability to reveal distinct behavioral patterns across subject groups.
The HSP model successfully identified subgroups with similar, yet not identical, nested decision-making behaviors.

Conclusions:

The proposed HSP model provides a unique and effective framework for exploratory analysis of mouse-tracking data.
It advances bi-clustering methods by allowing for dissimilar nested partitions within subject groups.
This approach facilitates the discovery of nuanced individual differences in decision-making and neurobehavioral responses.