The acronym DBSCAN stands for density-based spatial clustering of applications with noise. It sees clusters as areas of high density separated by areas of low density. This allows it to deal with clusters of any shape. This is in contrast to the K-means algorithm, which assumes clusters to be convex; that is, data blobs with centroids. The DBSCANalgorithm starts by identifying the core samples. These are points that have at least min_samples around them within a distance of eps (ε). Initially, a cluster is built out of its core samples. Once a core sample has been identified, its neighbors are also examined and added to the cluster if they meet the core sample criteria. Then, the cluster is expanded so that we can add non-core samples to it. These are samples that can be reached directly from the core samples within a distance...
DBSCAN
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