DBSCAN
Published:
The note builds DBSCAN from \(\varepsilon\)-neighborhoods and core points, grows each cluster outward from a core point, contrasts it with K-means, whose cluster count is an input rather than a result, and picks \(\varepsilon\) from the elbow of the sorted \(m\)-th smallest distances, checked against noise and largest-cluster shares.