– K-means:
– Assumes spherical clusters, sensitive to outliers.
– Not density-sensitive, requires specifying K.
– Suitable for well-defined, spherical clusters.
– Generally computationally less expensive.
– DBSCAN:
– Handles arbitrary cluster shapes, robust to outliers.
– Density-sensitive, less dependent on specifying clusters.
– Effective for irregularly shaped clusters with varying densities.
– Can be more computationally expensive.
Summary:
- Cluster Shape:
- K-means assumes spherical clusters; DBSCAN can handle arbitrary shapes.
- Outlier Handling:
- K-means is sensitive to outliers; DBSCAN is robust to outliers.
- Density Sensitivity:
- K-means is not sensitive to density variations; DBSCAN is density-sensitive.
- Parameter Dependency:
- K-means requires specifying the number of clusters (K); DBSCAN is less dependent on this.
Choose K-means for spherical clusters with minimal noise, and DBSCAN for irregularly shaped clusters with varying densities and robustness to outliers.