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Clustering
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DBSCAN
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163.
DBSCAN vs K-means Advantage
easy
What is a key advantage of DBSCAN over K-means?
A
DBSCAN requires fewer hyperparameters than K-means since epsilon is estimated automatically from the data
B
DBSCAN always produces more compact clusters than K-means by using density rather than distance
C
DBSCAN scales better than K-means to large datasets since it does not require centroid recomputation
D
DBSCAN can discover clusters of arbitrary shape and automatically identifies outliers as noise
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