What are the limitations of the DBSCAN algorithm?
DBSCAN has several limitations, including difficulty with varying densities, sensitivity to parameters, and challenges with high-dimensional data.
Can you explain how varying densities affect DBSCAN's performance?
Certainly! DBSCAN struggles when clusters have different densities because it relies on a fixed distance parameter to define clusters.
What happens if I choose the wrong parameters for DBSCAN?
Choosing inappropriate parameters can lead to poor clustering results, such as merging distinct clusters or failing to identify any clusters.
Are there any alternatives to DBSCAN that handle these limitations better?
Yes, alternatives like HDBSCAN or OPTICS can handle varying densities and are more robust in high-dimensional spaces.
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