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DBSCAN is a clustering algorithm that identifies clusters based on the density of data points, using parameters Eps (ε) for neighborhood distance and MinPts for minimum points to define a cluster. It classifies points into core, boundary, and noise categories, allowing it to effectively handle clusters of arbitrary shapes and outliers. The silhouette score is a metric for evaluating clustering quality, with values ranging from -1 to 1 indicating the separation and correctness of clusters.
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