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In DBSCAN, determining the epsilon parameter is often tricky.

Yet, the Elbow curve is often helpful in determining it.

To begin, 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.

**Why does it work?**

Recall that we are measuring the distance to a specific (kth) neighbor for all points.

Thus, the elbow point suggests a distance to a more isolated point or a point in a different cluster.

The point where change is most pronounced hints towards an optimal epsilon.

The efficacy is evident from the image above.

Selecting the elbow value provides better clustering results over another value.

π Over to you: What methods do you use to find an optimal epsilon for DBSCAN?

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I like to explore, experiment and write about data science concepts and tools. You can connect with me on LinkedIn and Twitter.