DBSCAN Clustering algorithm pseudocode

 

1. Start with a dataset of n points.

2. Choose a point p at random from the dataset.

3. If p has not been visited yet, mark it as visited.

4. Find all points in the dataset that are within a distance eps (epsilon) of p.

5. If there are less than minPts points within distance eps of p, mark p as noise and go to step 2.

6. Otherwise, mark p as the start of a new cluster.

7. Add all points within distance eps of p to the cluster.

8. For each point q in the cluster:
   a. If q has not been visited yet, mark it as visited.
   b. Find all points in the dataset that are within distance eps of q.
   c. If there are minPts or more points within distance eps of q, add them to the cluster.
   d. If q is not already a member of another cluster, add it to the current cluster.

9. Repeat steps 2-8 until all points have been visited.

10. Output the clusters.