DBSCAN Algorithm Visualization

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm that groups together points that are closely packed while marking points in low-density regions as outliers (noise).

Algorithm Implementation

The visualization follows the three main steps of DBSCAN:

  1. Core Point Identification
    • Finds points with sufficient neighbors within ε radius
    • Marks them as core points (red)
  2. Cluster Formation
    • Connects core points that are within ε distance
    • Forms clusters through density connectivity
  3. Border Point Assignment
    • Assigns non-core points to nearby clusters
    • Marks isolated points as noise
Parameters
100
4
60
Ready to start