DBSCAN Clustering Algorithm #machinelearning #ai #cluster #unsupervisedlearning #DBSCANClustering

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm that can identify clusters of varying shapes and sizes in a dataset, while also being robust to outliers. Here's how it works: Density-Based: DBSCAN groups together closely packed points based on a density criterion. It defines two parameters: Epsilon (ϵ): A radius around each point within which to search for neighboring points. MinPts: The minimum number of points required within the ε neighborhood of a point for it to be considered a core point. Core Points: A point is considered a core point if there are at least MinPts points (including itself) within its ε neighborhood. Border Points: A point is considered a border point if it's not a core point but falls within the ε neighborhood of a core point. Noise Points: Points that are neither core points nor border points are considered noise points or outliers.