Gaussian Mixture Models vs DBSCAN · Probabilistic vs Density Clustering

Gaussian Mixture Models (GMM) and DBSCAN represent two fundamentally different approaches to clustering: probabilistic (GMM) vs density-based (DBSCAN).

Key differences: GMM assigns every point a probability of belonging to each cluster (soft clustering). DBSCAN assigns hard labels + noise classification. GMM assumes clusters are Gaussian-distributed (elliptical/spherical). DBSCAN makes no shape assumption beyond density connectivity. GMM requires specifying the number of components (k). DBSCAN determines the number of clusters from the data.

Use GMM when clusters are approximately Gaussian and soft assignment is valuable (e.g., topic modeling, image segmentation). Use DBSCAN when clusters have arbitrary shapes or noise identification matters (spatial data, anomaly detection, exploratory analysis).