EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python

EvoCluster is an open-source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and customizable implementation of the metaheuristic based clustering algorithms which can be utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. EvoCluster can be extended by designing other optimizers, including more objective functions, adding other evaluation measures, and using more data sets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, and twelve evaluation measures. Useful Links The source code can be found on GitHub (http://evo-ml.com/2019/10/25/evocluster/) The Google colab copy (https://github.com/RaneemQaddoura/EvoCluster/blob/master/EvoCluster.ipynb) The documentation of the source code (http://evo-ml.com/evocluster-source-code-documentation/) Published Paper (https://link.springer.com/chapter/10.1007/978-3-030-43722-0_2)