Density-based clustering uses the idea of density reachability and density connectivity, which makes it very useful in discovering a cluster in nonlinear shapes. Density-based clustering takes two parameters into account--`eps` and `MinPts`. The `eps` parameter stands for the maximum radius of the neighborhood; `MinPts` denotes the minimum number of points within the eps neighborhood. Some of the common terminology associated with DBSCAN algorithm is as follows:

A point *A* in the dataset, with a neighbor count greater than or equal to `MinPts`, is referred to as a **core point**. The point *A* is called a **border point** if the number of its neighbors is less than `MinPts`, but belongs to the ϵ-neighborhood of some core point *C*. Finally, if a point ...