Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm, proposed by Martin Ester et al., 1996. The algorithm finds neighbors of data points, within a circle of radius ε, and adds them into same cluster. For any neighbor point, which its ε-neighborhood contains a predefined number of points, the cluster is expanded to contain its neighbors, ...

Clustering is an unsupervised machine learning task and many real world problems can be stated as and converted to this kind of problems. Clustering is grouping a set of  data objects is such a way that similarity of members of a group (or cluster) is maximized and on the other hand, similarity of members in two different groups, is minimized. ...