Expand description
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 = "0.1"Structs§
- Brute
Scan - A point collection where queries are answered via brute-force scans over the whole list.
- Brute
Scan Neighbours - 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).
- Kmeans
Builder - A builder for k-means to provide control over parameters for the algorithm.
- Optics
- Clustering via the OPTICS algorithm[1].
- Optics
Dbscan Clustering - An iterator over clusters generated by OPTICS using a DBSCAN-like criterion for clustering.
Traits§
- List
Points - 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.
- Region
Query - Collections of points that can be queried to find nearby points.