Source code of HG-means clustering, an efficient hybrid genetic algorithm proposed for the minimum sum-of-squares clustering (MSSC). This population-based metaheuristic uses K-means as a local search in combination with crossover, mutation, and diversification operators.
As HG-means algorithm uses K-means, we included the fundamental source files of the fast K-means implementation of Greg Hamerly (to whom we are grateful for making the source code available) in this repository, under the folder
/hamerly. Original files and complete source code of Greg Hamerly K-means can be found at: https://github.com/ghamerly/fast-kmeans.
For the exact crossover, HG-means uses the implementation of Dlib (https://github.com/davisking/dlib) for solving an assignment problem. Dlib files are included in
HG-means clustering is available as a C++ code, as well as a Python package.
HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering. D. Gribel and T. Vidal, 2019. Pattern Recognition, https://doi.org/10.1016/j.patcog.2018.12.022
Installation and Run
To run the algorithm in C++, go to
/hgmeans folder and try the following sequence of commands:
> ./hgmeans 'dataset_path' pi_min n2 [nb_clusters]
> ./hgmeans 'data/iris.txt' 10 5000 2 5 10
This script executes HG-means clustering for "iris" dataset, with 10 solutions in population, a maximum of 5000 iterations, and 2, 5 and 10 clusters.
Important: You can provide a ground-truth file with the labels of clusters. In this case, make sure that a file with the same name of the dataset and '.label' extension is placed in the same folder of the dataset. If this file is provided, HG-means clustering will compute clustering performance metrics. See section Data format to check the expected data format for datasets and labels files.
Parameters of the algorithm
dataset_path: The path of dataset.
pi_min (default = 10): Population size. Determines the size of the population in the genetic algorithm.
n2 (default = 5000): Maximum number of iterations. Determines the total number of iterations the algorithm will take.
[nb_clusters]: The list with number of clusters. You can pass multiple values, separated by a single space.
HG-means is also available as a Python package. To install HG-means, run the following installation command:
> python -m pip install hgmeans
For Windows users that do not have a C++ compiler, it may be required an installation of C++ Build tools, which can be downloaded here: https://go.microsoft.com/fwlink/?LinkId=691126
That is it! Now, open your Python interface, import the package and create an instance of HG-means. To execute it, just call function
Go() with the corresponding parameters. See an example below:
>>> import hgmeans
>>> my_demo = hgmeans.PyHGMeans()
>>> my_demo.Go('data/iris.txt', 10, 5000, [2,5,10])
This script executes HG-means algorithm for "iris" dataset, with 10 solutions in population, a maximum of 5000 iterations, and 2, 5 and 10 clusters. Here the number of clusters is passed in an array, so values are separated by commas.
Dataset files. In the first line of a dataset file, the number of data points (n) and the dimensionality of the data (d) is set, separated by a single space. The remaining lines correspond to the coordinates of data points. Each line contains the values of the d features of a sample, where x_ij correspond to the j-th feature of the i-th sample of the data. Each feature value is separated by a single space, as depicted in the scheme below:
Some datasets are provided in
/data folder in HG-means repository.
Labels files. The content of a labels file exhibits the cluster of each sample of the dataset according to ground-truth, where y_i correspond to the label of the i-th sample:
Important: Labels files must have the '.label' extension. Some labels are provided in
/data folder in HG-means repository.