Nature

scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great ...
Nature

Significance analysis for clustering with single-cell RNA-sequencing data

Unsupervised clustering is widely applied in single-cell RNA-sequencing (scRNA-seq) workflows. The goal is to detect distinct cell populations that can be annotated as known cell types or discovered ...
Search Engine Land

10 types of data that should be on your keyword clustering wish list

Use SERP data for keyword clustering to understand why URLs rank, what entities are used and the PAA/FAQ to answer. Everybody is talking about keyword clusters. At the core, it’s pretty simple – group ...
Visual Studio Magazine

K-Means Data Clustering from Scratch Using C#

K-means is comparatively simple and works well with large datasets, but it assumes clusters are circular/spherical in shape, so it can only find simple cluster geometries. Data clustering is the ...
Visual Studio Magazine

Data Clustering Using a Self-Organizing Map (SOM) with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on technique for visualizing and clustering data. A self-organizing map (SOM) is a data structure that can be used ...
BizTech

What Are the Benefits of Unsupervised Machine Learning and Clustering?

Nathan Eddy works as an independent filmmaker and journalist based in Berlin, specializing in architecture, business technology and healthcare IT. He is a graduate of Northwestern University’s Medill ...