Density-based clustering

Density-based clustering is a method that groups data points based on how closely they are packed together. Imagine a landscape where areas with many points are like dense forests, and sparse areas are deserts. The algorithm identifies these dense regions as clusters and can handle groups of various shapes and sizes. It also naturally ignores noisy or outlier points that don't belong to any dense area. This approach is useful when data has irregular patterns, making it flexible and effective for tasks like detecting unusual behavior or complex structures in data.