DBSCAN
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm in data science that groups together points that are close to each other based on a density criterion. It is a non-parametric algorithm that does not require the number of clusters to be specified beforehand. DBSCAN works by defining a neighborhood around each point and identifying core points, which have a minimum number of points within their neighborhood. Points that are not core points but are within the neighborhood of a core point are considered border points, while points that are not core points and are not within the neighborhood of a core point are considered noise points. DBSCAN is particularly useful for datasets with complex structures and varying densities, and it can handle noise and outliers effectively.
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