DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, is a powerful clustering algorithm that groups data points based on their density. It was introduced by Ester et al. in 1996 and is particularly effective for identifying clusters of varying shapes and sizes, as well as detecting noise or outliers in the data.

The algorithm operates using two key parameters: epsilon (ε), which defines the radius of a neighborhood around a point, and minPts, which specifies the minimum number of points required to form a dense region. Points are classified into three categories: core points (which have at least minPts within ε), border points (which are within ε of a core point but do not meet the minPts requirement), and noise points (which are neither core nor border points) ¹⁴.

DBSCAN is advantageous because it does not require the number of clusters to be specified in advance, unlike k-means clustering. This makes it suitable for exploratory data analysis where the underlying structure of the data is unknown ²³.