Crate cogset

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.

Dbscan offers density-based clustering via the DBSCAN algorithm.

Source.

Installation

Add the following to your Cargo.toml file:

[dependencies]
cogset = "0.1"

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 BruteScan.
Dbscan	Clustering via the DBSCAN algorithm[1].
Euclid	Points in ℝn with the L2 norm.

Traits

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.