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k-means forces everything into round blobs and assigns even the noise a home. DBSCAN won't. DBSCAN clusters by density: it grows clusters from regions where points are packed tightly and labels the sparse leftovers as noise. It doesn't ask how many clusters. A point with enough neighbors inside a radius is a core point, and clusters spread out ...
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