# Crate cogset [stability]
[-] [+]
[src]

Clustering algorithms.

This crate provides generic implementations of clustering
algorithms, allowing them to work with any back-end "point
database" that implements the required operations, e.g. one might
be happy with using the naive collection `BruteScan`

from this
crate, or go all out and implement a specialised R*-tree for
optimised performance.

Density-based clustering algorithms:

- DBSCAN (
`Dbscan`

) - OPTICS (
`Optics`

)

Others:

*k*-means (`Kmeans`

)

# Installation

Add the following to your `Cargo.toml`

file:

```
[dependencies]
cogset = "0.2"
```

## Structs

BruteScan | A point collection where queries are answered via brute-force scans over the whole list. |

BruteScanNeighbours | An iterator over the neighbours of a point in a |

Dbscan | Clustering via the DBSCAN algorithm[1]. |

Euclid | Points in ℝ |

Kmeans | Clustering via the |

KmeansBuilder | A builder for |

Optics | Clustering via the OPTICS algorithm[1]. |

OpticsDbscanClustering | An iterator over clusters generated by OPTICS using a DBSCAN-like criterion for clustering. |

## Traits

Euclidean | |

ListPoints | Collections of points that can list everything they contain. |

Point | A point in some (metric) space. |

Points | A data structure that contains points of some sort. |

RegionQuery | Collections of points that can be queried to find nearby points. |