DBSCAN
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular unsupervised machine learning algorithm used for clustering data points based on their density in a feature space. It groups together points that are closely packed, marking outliers as noise, and does not require the number of clusters to be specified in advance. This makes it effective for discovering clusters of arbitrary shapes and handling noise in datasets.
Developers should learn DBSCAN when working with spatial data, anomaly detection, or datasets where clusters have varying densities and shapes, such as in geographic information systems, image segmentation, or customer segmentation. It is particularly useful in scenarios where traditional clustering methods like K-means fail due to non-spherical clusters or the presence of outliers, as it can identify noise points and adapt to complex data structures without prior knowledge of cluster counts.