DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, is a popular clustering algorithm used in machine learning that identifies clusters based on the density of data points in a given area. This method is particularly effective for discovering clusters of varying shapes and sizes while also effectively distinguishing outliers or noise. Its ability to work with large datasets makes it a valuable technique for quantitative analysis in various applications such as market segmentation, image processing, and geospatial data analysis.
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