While k-means clustering relied on providing the number of clusters beforehand, the DBSCAN algorithm is a non-parametric algorithm. Given a set of points, DBSCAN groups together points that are close to each other while also marking outliers. This algorithm can identify clusters even in large spatial datasets by simply highlighting the local density of points. It is also one of the most widely used clustering algorithms, especially for location data. DBSCAN requires two parameters to be supplied before running the algorithm: epsilon and minimum points or samples. Their values significantly influence the results of this algorithm and therefore require some fine-tuning, as well as exploration, before finding suitable clusters.
Epsilon is the parameter that specifies the radius of a neighborhood with respect to other points...
