Mini toolsDefinition
Density-Based Spatial Clustering of Applications with Noise: A clustering algorithm that groups points based on density.
Detailed Explanation
DBSCAN identifies clusters as dense regions separated by regions of lower density. It requires two parameters: epsilon (neighborhood distance) and minPts (minimum points for core point). The algorithm can find arbitrarily shaped clusters and automatically identifies noise points.
Use Cases
1. Spatial data analysis 2. Anomaly detection 3. Geographic clustering 4. Network analysis
