cogset 0.2.0

Generic implementations of clustering algorithms. Includes k-means, DBSCAN and OPTICS.

Crate cogset [] [src]

Clustering algorithms.

A cluster

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)

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.

Kmeans

Clustering via the k-means algorithm (aka Lloyd's algorithm).

KmeansBuilder

A builder for k-means to provide control over parameters for the algorithm.

Optics

Clustering via the OPTICS algorithm[1].

OpticsDbscanClustering

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

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.