DBSCAN
A density-based clustering algorithm that groups together closely packed points while marking points that lie alone in low-density regions as noise outliers.
Think of It Like This
Like finding cities on a map by looking for areas with many buildings close together, while ignoring isolated cabins in the middle of nowhere.
Unlike K-Means, DBSCAN does not require specifying the number of clusters in advance and can discover clusters of arbitrary shapes. It relies on two parameters: the neighborhood radius and the minimum number of points required to form a dense region. This makes it highly effective for spatial data analysis and robust against noisy datasets.