Data clustering remains an essential component of unsupervised learning, enabling the exploration and interpretation of complex datasets. The field has witnessed considerable advancements that address ...
Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, ...
A hybrid method for clustering multivariate observations is proposed, which combines elements of the k-means and the single-linkage clustering techniques. One purpose of the proposed method is to ...
3monon MSN
Novel machine learning-based cluster analysis method that leverages target material property
In materials science, substances are often classified based on defining factors such as their elemental composition or ...
Monitoring brain injury biomarkers and glucose variation in patients who have suffered an acute cranial injury during the entire first week of hospitalisation can provide a more accurate picture of ...
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 new study ...
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