Why is DBSCAN often preferred for data with clusters of varying shapes and sizes?
- It depends on density rather than distance
- It relies on statistical modeling
- It requires manual setting for each cluster shape
- It uses fixed-size clusters
DBSCAN is preferred for data with clusters of varying shapes and sizes because it depends on density rather than a specific distance metric. This means that DBSCAN can identify clusters with arbitrary shapes and sizes based on the density of data points within a region, rather than relying on a fixed distance or shape constraint. This makes it versatile for complex clustering tasks.
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