# Crate dbscan[−][src]

# A Density-Based Algorithm for Discovering Clusters

This algorithm finds all points within `eps`

distance of each other and
attempts to cluster them. If there are at least `mpt`

points reachable
(within distance `eps`

) from a given point P, then all reachable points are
clustered together. The algorithm then attempts to expand the cluster,
finding all border points reachable from each point in the cluster

See `Ester, Martin, et al. "A density-based algorithm for discovering clusters in large spatial databases with noise." Kdd. Vol. 96. No. 34.`

for the original paper

Thanks to the rusty_machine implementation for inspiration

## Structs

Model |
DBSCAN parameters |

## Enums

Classification |
Classification according to the DBSCAN algorithm |

## Functions

cluster |
Cluster datapoints using the DBSCAN algorithm |

euclidean_distance |
Calculate euclidean distance between two vectors |