DBSCAN, point by point


Dataset
Radius (ε) 30 px
minPts counting the point itself
Run

The one question.
DBSCAN never computes a center. It only ever asks, of one point at a time: are there at least minPts points within ε of me? If yes, that point is core and the cluster grows through it — every point inside its circle is swallowed, and each of those gets asked the same question. If no, the point is parked as noise, though a later cluster may still reach it and claim it as a border point. Every point ends up as exactly one of three things.

KindTestDrawn as
Core at least minPts points within ε, counting itself a filled dot — the cluster expands through it
Border not crowded itself, but inside some core point’s circle a hollow ring — it joins the cluster but never expands it
Noise neither a small gray cross — it belongs to nothing

Things worth trying:
1. Two rings, ε = 30. Press Run and watch one cluster crawl the whole way around a ring and stop. This is the case k-means cannot do at any k and failed.

2. Smiley, ε = 30. Three clusters and nobody supplied k: two eyes and a mouth, with the long curve kept in one piece — the exact answer k-means cannot reach at any k.

3. Elongated — two long thin clusters, the identical points used on the k-means and GMM pages. What's the difference?