Here’s the tabular comparison with K-means, Hierarchical Clustering, and DBSCAN in the requested order:
| Aspect | K-means | Hierarchical Clustering | DBSCAN |
|---|---|---|---|
| Clustering Approach | Partitioning | Agglomerative or Divisive | Density-based |
| Shape of Clusters | Spherical, equally sized | Various shapes (depends on linkage) | Arbitrary shapes |
| Number of Clusters | Requires specifying K beforehand | No predefined K required | No predefined K required |
| Handling Noise | Sensitive to outliers and noise | Can handle noisy data | Robust to noise; classifies as noise |
| Parameter Sensitivity | Sensitive to initial centroid placement | Depends on linkage method and distance | Sensitive to MinPts and ε |
| Scalability | Suitable for small to moderate datasets | Can be computationally intensive | Effective for large datasets |
| Use Cases | Spherical clusters, known K or estimated | Hierarchical data structures, exploration | Irregular clusters, varying sizes |
Please note that the choice of clustering algorithm depends on your specific data characteristics and goals, and it’s often valuable to try different algorithms to determine which one works best for your particular dataset and problem.
Happy Clustering!