Abstract: In this letter, different unsupervised machine learning (ML)-based user clustering algorithms, including K-Means, agglomerative hierarchical clustering (AHC), and density-based spatial ...

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) ...

This article explores the top 10 ML algorithms essential for quality assurance, from Decision Trees for defect prediction to Neural Networks for automated test generation, helping test engineers ...

This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the ...

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 ...