In a case where you have a dataset with numerous outliers, which clustering algorithm would you choose and why?
- DBSCAN due to robustness to outliers
- DBSCAN due to sensitivity to noise
- K-Means due to robustness to noise
- K-Means due to sensitivity to outliers
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) would be suitable since it's robust to outliers. It can identify dense clusters and leave outliers as unclassified, making it effective in such scenarios.
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