naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation

Abstract:We present an overview of R package called \texttt{naspaclust}, a tool for spatial fuzzy clustering improvement by hybridising the Fuzzy Geographically Weighted Clustering (FGWC) with nature-inspired optimisation algorithms. There are seven optimisation algorithms in general, namely Artificial Bee Colony, Flower Pollination Algorithm, Gravitational Search Algorithm, Harris-Hawk Optimisation, Intelligent Firefly Algorithm, Particle Swarm Optimisation, and Teaching-Learning Based optimisation. The optimisation algorithms available do not only use the basic algorithms, but also the modified version of several parts such as randomisation terms and updating rules. Furthermore, we also provide additional options by providing more distance metrics options based on the \texttt{ rdist} package. This study showed the implementation of the \texttt{naspaclust} package in various cases, as well as comparing the performance of the algorithms in the scope of basic algorithms. The \texttt{naspaclust} package allows to improve the performance of fuzzy clustering, mainly in spatial context.

Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)

arXiv:2610.16356 [cs.NE]

(or arXiv:2610.16356v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2610.16356 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.