Which is the best clustering algorithm?

Question

Accepted Answer

There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.

The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.
K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.
DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional data.
Hierarchical clustering is useful for visualizing cluster relationships, but can be computationally expensive.
Consider using a combination of algorithms or ensemble methods for better results.
Evaluate performance using metrics like silhouette score, Davies-Bouldin index, or visual inspection of clusters.

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