Clustering

Clustering algorithms group similar data points without needing labeled examples. They discover natural groupings by measuring similarities between observations.

Example: Imagine arranging library books by similarities rather than pre-assigned categories. Approaches include K-means (dividing data into K clusters), hierarchical clustering (nested groupings), and DBSCAN (density-based clusters for irregular shapes). Applications span customer segmentation, document categorization, image compression, and anomaly detection.