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What is DBSCAN Clustering Explained in ML about?
DBSCAN is a density-based clustering algorithm that identifies clusters of arbitrary shapes and effectively handles noise and outliers. It categorizes data points into core, border, and noise points, using parameters like eps and MinPts to define cluster density. Unlike K-Means, DBSCAN does not require specifying the number of clusters in advance and performs better with complex datasets.
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- VvnaikcseVvnaikcse
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- EN