Unsupervised learning is valuable in epidemiology for several reasons: 1. Data Exploration: It helps in exploring large datasets to find unknown correlations and patterns. 2. Clustering: Techniques like K-means clustering can group similar cases or regions, which can be critical in identifying outbreaks or high-risk areas. 3. Anomaly Detection: Methods like Principal Component Analysis (PCA) can identify outliers that may indicate unusual disease occurrences. 4. Dimensionality Reduction: Techniques such as t-SNE reduce the complexity of data, making it easier to visualize and interpret.
