C++ implementation of the DBSCAN clustering algorithm, originally adopted from https://github.com/siddharth-agrawal/DBSCAN but completely rewritten. Uses nanoflann for kd-tree radius search.

Inputs are:

• points: A M x N matrix where M is the number of points and N the dimensionality.
• eps: Radius around point to be considered a neighbor.
• min_pts: Minimum number of points in neighborhood to be considered a base point.

The output is:

• cluster: An M x 1 vector of class labels. 0 labels don't belong to any cluster and can be considered outliers.