Sensitivity to Parameter Choices:
DBSCAN requires configuring hyperparameters like ε (maximum distance defining a point’s neighborhood) and the minimum points to establish a dense region. These choices significantly influence resulting clusters. K-means, requiring the number of clusters (K), is generally easier to determine, as it directly reflects the desired cluster count. DBSCAN’s abstract parameters introduce sensitivity to value selection.
Boundary Points and Noise:
DBSCAN explicitly identifies noise points (those not belonging to any cluster) and handles outliers well. However, the delineation of boundary points within DBSCAN can be arbitrary. In K-means, points at cluster boundaries may be assigned to neighboring clusters, potentially causing instability when a point is close to the boundary shared by two clusters