A python baesed interpretable clustering method based on the topology of a dataset, using novelty search with local competition. This method is outlined in the GEECO 24 poster entitled ATOMIC: an Interpretable Clustering Method Based on Data Topology.
The evolutionary portion of algorithm is implemented using the DEAP library which can be found at https://deap.readthedocs.io/en/master/about.html and in “DEAP: Evolutionary Algorithms Made Easy” in Journal of Machine Learning Research, pp. 2171-2175, no 13, jul 2012 by Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné.
For a full acounting of this methodology and results when applied to UCI machine learning becnhmark dataset see our paper at: [LINK]