ML: Clustering

This category focuses on unsupervised learning methods for uncovering structure in unlabeled datasets by grouping similar data points. Techniques such as k-means, hierarchical clustering, density-based models, and deep clustering frameworks are used to explore data, generate hypotheses, and enable tasks like patient stratification. Example applications include identifying novel disease subtypes from multi-omics data with spectral clustering, discovering gene modules with shared expression across tissues, and grouping patients based on metabolomics profiles using deep embedded clustering.