Now we will introduce you to DBSCAN, a density-based clustering technique. It's a very simple technique. It selects a random point; if the point is in a dense area (if it has more than N neighbors do), it starts growing the cluster, including all the neighbors, and the neighbors of the neighbors, until it reaches a point where there are no more neighbors.
If the point is not in a dense area, it is classified as noise. Then, another unlabeled point is selected randomly and the process starts over. This technique is great for non-spherical clusters, but it works equally well with spherical ones. The input is just the neighborhood radius (the eps parameter, that is, the maximum distance between two ...
