Unsupervised Clustering
Cross-source consensus on Unsupervised Clustering from 1 sources and 4 claims.
1 sources · 4 claims
Uses
How it works
Comparisons
Highlighted claims
- Cluster-number selection combined silhouette scores with dendrogram inspection. — An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring
- The study retained k = 3 for macro-level monitoring and k = 5 for finer etiological differentiation. — An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring
- Ward hierarchical clustering was selected over K-Means because it was considered more robust for heterogeneous small datasets. — An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring
- Ward hierarchical clustering was more stable than K-Means in the small real-world cohort. — An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring