Unsupervised Learning Explained: Clustering and Dimensionality Reduction Basics

Unsupervised machine learning allows algorithms to find patterns, groups, and relationships in data without predefined labels or answers. It is particularly valuable when large datasets exist but lack annotated information, helping organizations uncover hidden insights. Clustering, one of its core techniques, groups similar data points together and has applications ranging from customer segmentation and fraud detection to healthcare analysis. Hierarchical clustering, which includes agglomerative (bottom-up) and divisive (top-down) approaches, organizes data into nested groups visualized through tree-like diagrams called dendrograms. DBSCAN is another clustering method that groups data based on density, treating isolated points as noise or outliers.