DBScan is a data clustering algorithm that finds a number of clusters starting from the estimated density distribution of corresponding nodes.
DBScan requires two parameters: an epsilon and the minimum number of points required to form a cluster. It starts with an arbitrary starting point that has not been visited. This point's epsilon-neighborhood is retrieved, and if it contains sufficiently many points, a cluster is started. Otherwise, the point is labeled as noise. Note that this point might later be found in a sufficiently sized epsilon-environment of a different point and hence be made part of a cluster.
If a point is found to be a dense part of a cluster, its epsilon-neighborhood is also part of that cluster. Hence, all points that are found within that epsilon-neighborhood are added, as is their own epsilon-neighborhood when they are also dense. This process continues until the density-connected cluster is completely found. Then, a new unvisited point is retrieved and processed, leading to the discovery of a further cluster or noise.
An example of how to use the DBScan implementation can be found in the Sources/main.m file.
Christian Vogel
Usage is provided under the MIT License.