K-Means Algorithm, Influenza Transmission, Cluster Analysis, Urban Characteristics Share and Cite: Ye, S. (2025) Application ...

Unsupervised machine learning algorithms, such as clustering and anomaly detection, work by identifying patterns and anomalies in data without the need for labeled training data. These algorithms are ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Clustering can be done using various algorithms such as k-means, hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM) ...

A new study introduced a machine learning-powered clustering model that incorporates both basic features and target properties, successfully grouping over 1,000 inorganic materials.

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A Tokyo Tech ...

Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using ...