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
Density-based clustering algorithms:
- DBSCAN (
- OPTICS (
- k-means (
Add the following to your
[dependencies] cogset = "0.1"
A point collection where queries are answered via brute-force scans over the whole list.
An iterator over the neighbours of a point in a
Clustering via the DBSCAN algorithm.
Points in ℝn with the L2 norm.
Clustering via the k-means algorithm (aka Lloyd's algorithm).
A builder for k-means to provide control over parameters for the algorithm.
Clustering via the OPTICS algorithm.
An iterator over clusters generated by OPTICS using a DBSCAN-like criterion for clustering.
Collections of points that can list everything they contain.
A point in some (metric) space.
A data structure that contains points of some sort.
Collections of points that can be queried to find nearby points.