How does DBSCAN differentiate between border points and noise points?
- By analyzing their density within Epsilon radius
- By assigning different weights to them
- By clustering them separately
- By the number of points within Epsilon distance and their relation to MinPts
In DBSCAN, border points have fewer than MinPts within their Epsilon radius but are reachable from a core point. Noise points don't satisfy either condition. Differentiating between these allows DBSCAN to form clusters without being influenced by noise and to create clusters of varying shapes by including border points.
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