How does DBSCAN handle noise in the data, and what distinguishes it from other clustering methods?
- Classifies Noise as a Separate Cluster
- Considers Noise in Cluster Formation
- Handles Noise Through Density-Based Clustering
- Ignores Noise
DBSCAN handles noise by classifying it as a separate category and distinguishes itself by utilizing a density-based approach that groups together points that are closely packed, considering the rest as noise.
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