k-Nearest Neighbors (k-NN) is a method that finds nearby data points based on distance. In some cases, you might want to find all the points within a certain distance from a specific point, and that’s where ε (epsilon) comes in.

Epsilon (ε) is like a boundary or a distance limit. You use ε to say, Find all the points within ε distance of this point. It helps you define a neighborhood around your point.

If you set ε small, you’ll only find points very close to your point. If you set ε big, you’ll find farther away points. It’s a way to adjust how far you want to look for neighbors.

So, k-NN with ε helps you find points that are not just the k closest ones, but all the points that fall within a specific distance from your chosen point. It’s useful for tasks where you care about a certain range of proximity in your data.

• An epsilon (ε) value of 13 means that are considering points within a distance of 13 units from each other as part of the same neighborhood. This value defines how densely packed your clusters are in terms of proximity.
• A minPts value of 40 sets a minimum number of data points required within the ε-distance to form a cluster. In other words, to be considered a cluster, a group of points must have at least 40 neighbors within the 13-unit distance.

These parameter values indicate that are looking for relatively large and dense clusters in data. When running DBSCAN with these values, will identify clusters that meet these criteria.