RecombinatorKMeans.jl

This code implements the recombinator-k-means method described in the paper "Recombinator-k-means: An evolutionary algorithm that exploits k-means++ for recombination" by C. Baldassi submitted for publication, (2019) (arXiv).

The code is written in Julia. It requires Julia 1.6 or later.

It provides four main optimization methods, which are exported from the package:

• kmeans is a standard implementation of Lloyd's algorithm for k-means; it can use either uniform of k-means++ initialization (the latter in the improved version that is also used by scikit-learn)
• reckmeans is the recombinator-k-means method described in the paper
• gakmeans is the genetic algorithm with pairwise-nearest-neighbor crossover proposed in this paper
• rswapkmeans is the random-swap algorithm proposed in this paper

It also provides two functions to compute the centroid index as defined in this paper, an asymmetric one called CI and a symmetric one called CI_sym. These are not exported.

It also provides a function to compute the variation of information metric to quantify the distance between two partitions as defined in this paper. The function is called VI and is not exported.

To install the module, just clone it from GitHub into some directory. Then enter in such directory and run julia with the "project" option:

$ julia --project

(Alternatively, if you start Julia from some other directory, you can press ; to enter in shell mode, cd into the project's directory, enter in pkg mode with ] and use the activate command.)

The first time you do this, you will then need to setup the project's environment. To do that, when you're in the Julia REPL, press the ] key to enter in pkg mode, then resolve the dependencies:

(RecombinatorKMeans) pkg> resolve

This should download all the required packages. You can subsequently type test to check that everything works. After this, you can press the backspace key to get back to the standard Julia prompt, and load the package:

julia> using RecombinatorKMeans

The format of the data must be a Matrix{Float64} with the data points organized by column. (Typically, this means that if you're reading a dataset you'll need to transpose it. See for example the runfile.jl script in the test directory.)

These four functions are available once you load the package: kmeans, reckmeans, gakmeans and rswapkmeans. You can use the Julia help (press the ? key in the REPL) to see their documentation.

The reckmeans and gakmeans functions will run in parallel if there are threads available: either run Julia with the -t option or use the JULIA_NUM_THREADS environment variable.

For the purpose of complete reproducibility, you can check out the tag paper-v5 of the repository, which will get you the version of the code used to collect the results in the paper. Also, the repository includes a file "Manifest_20220103.toml" that specifies the exact version of the dependencies that were used. You can use it to overwrite your "Manifest.toml" file and then call instantiate in pkg mode to reproduce the same environment. Note that the version of Julia should be the same as that in the paper too.

The code is released under the MIT licence.

The k-means++ code was first written from scratch from the k-means++ paper, then improved after reading the corresponding scikit-learn's code, then heavily modified. The scikit-learn's version was first coded by Jan Schlueter as a port of some other code that is now lost.

The genetic algorithm code was written from scratch from the paper; the accompanying C code available at the repository was inspected to check some finer details of the behavior, but none of the code was used.