Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Confocal Fluorescence Microscopy

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison.

Briefings in bioinformatics·2024

Same author

Fibroblast-like synoviocyte targeting antibodies are associated with failure to reach early and sustained remission or low disease activity after first-line therapy in rheumatoid arthritis.

RMD open·2024

Same author

Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs.

Frontiers in bioinformatics·2024

Same author

BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation.

PloS one·2023

Same author

Novel maternal autoantibodies in autism spectrum disorder: Implications for screening and diagnosis.

Frontiers in neuroscience·2023

Same author

Exploring the Microbiome Analysis and Visualization Landscape.

Frontiers in bioinformatics·2022

Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026

Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026

Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026

Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026

Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026

Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 18, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

FLASC: a flare-sensitive clustering algorithm.

Daniël M Bot¹, Jannes Peeters¹, Jori Liesenborgs²

¹Data Science Institute (DSI), Universiteit Hasselt, Diepenbeek, Belgium.

Peerj. Computer Science

|June 26, 2025

Summary

This summary is machine-generated.

Flare-sensitive clustering (FLASC) identifies shape-based subgroups within data clusters by detecting branches. This novel algorithm enhances exploratory data analysis by revealing complex data structures, building upon existing density-based methods.

Keywords:

Branch-hierarchy detection Density-based clustering Exploratory data analysis HDBSCAN*

More Related Videos

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

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Related Experiment Videos

Last Updated: Sep 18, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

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

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Area of Science:

Data Science
Machine Learning
Computational Statistics

Background:

Clustering algorithms are essential for exploratory data analysis, grouping similar data points.
Cluster shape, such as Y-shaped formations, can signify evolving processes and distinct outcomes.
Existing density-based clustering methods like HDBSCAN* do not explicitly identify branched structures.

Purpose of the Study:

Introduce flare-sensitive clustering (FLASC), a new algorithm for detecting branches within data clusters.
Enable the identification of shape-based subgroups that represent meaningful data patterns.
Enhance the capabilities of density-based clustering for complex data analysis.

Main Methods:

FLASC builds upon the HDBSCAN* algorithm, incorporating a post-processing step to detect branches.
Branch detection is achieved through analysis of within-cluster connectivity.
Two variants of FLASC are presented, offering different trade-offs between computational cost and noise robustness.

Main Results:

FLASC variants demonstrate computational scaling comparable to HDBSCAN*.
The algorithm produces consistent outputs across multiple runs.
Branch detection using FLASC proves beneficial on two real-world datasets, revealing previously unidentified subgroups.

Conclusions:

FLASC effectively identifies branched structures within clusters, providing deeper insights into data.
The algorithm enhances exploratory data analysis by uncovering shape-based subgroups.
FLASC offers a valuable extension to density-based clustering, with implementations available in Python.