# How to Find Optimal Epsilon Value for DBSCAN Clustering?

## Metadata

- Author: Avi Chawla
- Full Title: How to Find Optimal Epsilon Value for DBSCAN Clustering?
- URL: how-to-find-optimal-epsilon-value

## Highlights

- DBSCAN has three hyperparameters:
- Epsilon: two points are considered neighbors if they are closer than Epsilon.
- min_samples: Min neighbors for a point to be classified as a core point.
- The distance metric. We can use the Elbow Curve to find an optimal value of Epsilon:

Set k as the min_samples hyperparameter. For every data point, plot the distance to its kth nearest neighbor (in increasing order). The optimal value of Epsilon is found near the elbow point. (View Highlight)