How does 'DBSCAN' clustering differ from 'K-means' and 'hierarchical' clustering?
- DBSCAN can find arbitrarily shaped clusters and is less affected by outliers
- DBSCAN creates a hierarchy of clusters
- DBSCAN requires the number of clusters to be specified
- DBSCAN uses centroid to form the clusters
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) differs from K-means and hierarchical clustering in that it can find arbitrarily shaped clusters, and it's less affected by outliers. It does not require the user to set the number of clusters a priori, but instead, it infers the number of clusters based on the data.
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