igarnier / clustering

Functorial k-clustering implementation in OCaml.

Provided algorithms:

• k-means (voronoi iteration + various initialisation methods)
• k-medoids (voronoi iteration or PAM + various initialisation methods)
• agglomerative hierarchical clustering

Some example code is available here:

https://github.com/igarnier/clustering/wiki/An-example-with-k-means

For some simple, typical distance measures used in maths, look at the gromov library.

Some examples are provided in lib_test/gaussian.ml (relies on owl, vplot and gromov). The library also provides sequential (and soon parallel) multi-start wrappers, as well as ways to assess the tradeoff between the quality of clustering and overfitting using provided cost functions.

BUILDING & INSTALLING

Building and installing the package relies on having the opam package manager installed.

1. Do opam install jbuilder batteries parmap.
2. Type jbuilder build at the root to build the package.
3. Type jbuilder install to install the package in your .opam directory.
4. (optional) To build the example in lib_test/gaussian.ml, you will need to opam install owl. Then, jbuilder runtest should build and run the example. The results should be available in _build/default/lib_test/dataset.png (to see the clusters as they have been generated) and _build/default/lib_test/result.png (to see the clusters as inferred by k-means)

TODOS:

1. Parmap doesn't work super well: all child processes run on the same CPU. Try with Netmcore.
2. Compare quality of clustering as well as performance with scikit-learn's implementation