The DBSCAN Clustering algorithm provides a way to group together similar data points. This similarity can be measured using any metric but Euclidean Distance is most commonly used.
The algorithm works roughly as follows:
There is a cluster when the number of points within distance ε is greater than some min points threshold. We go through each point and build clusters by finding points densely connected to it.
The above lets you test out a DBSCAN Classifier. Click to add new data points and hit 'Cluster' to run the classifier.