DBSCAN, Explained in 5 Minutes

DBSCAN is a clustering algorithm that identifies clusters in data based on the density of points, making it useful for handling noise and detecting outliers. Unlike k-means, DBSCAN doesn't require specifying the number of clusters in advance, which makes it advantageous in many situations. The algorithm uses two key parameters: the radius (epsilon) and the minimum number of neighbors (N) required to form a core point. Core points, along with their nearby neighbors, form clusters, while points that don't meet these criteria are labeled as noise or outliers. The implementation of DBSCAN starts with a distance function, often Euclidean, to compute distances between points. The algorithm iterates over all points, grouping them into clusters based on their vicinity to each other. Points that do not have enough neighbors are classified as noise. After implementing DBSCAN, the performance can be checked by comparing it with the results from the `sklearn` library, which should produce identical clusters. It is important to fine-tune the epsilon and N values, as they heavily influence the clustering results. The article provides an example with synthetic data to visualize the clustering process.