Density-Based Clustering is a method used in data analysis to group together points that are closely packed together, while marking points in low-density regions as outliers. This approach helps identify clusters of varying shapes and sizes, making it effective for real-world data that may not fit into simple geometric patterns. One popular algorithm for Density-Based Clustering is DBSCAN (Density-Based Spatial Clustering of Applications with Noise). It works by defining a neighborhood around each point and expanding clusters based on the density of points within that neighborhood. This allows for the discovery of clusters without needing to specify the number of clusters in advance.