When would you pick DBSCAN over k-means?
mediumAnswer
- Pick DBSCAN when clusters are non-convex or of very different densities, when you don't want to specify k, and when you want an explicit notion of noise/outliers. k-means struggles with irregular shapes and forces every point into a cluster.
- DBSCAN needs two parameters: eps (neighborhood radius) and .
Check yourself — multiple choice
- DBSCAN requires setting k explicitly
- DBSCAN handles arbitrary-shape clusters and detects noise
- k-means handles noise better than DBSCAN
- DBSCAN cannot detect outliers
DBSCAN finds arbitrary shapes and labels low-density points as noise.
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