Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Dependencies
Introduction
Density-based spatial clustering of applications with noise (DBSCAN) is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.
In the Notebook, we learn how to use scikit-learn to implement DBSCAN.