Spatial fuzzy clustering improved with nature inspired algorithms
naspaclust: A hybridisation of spatial fuzzy clustering with nature-inspired optimisation
Neural and Evolutionary ComputingMachine Learning
Summary
Clustering is a way to group similar items, but it can be tricky when locations matter. The authors introduce an R software tool called naspaclust that combines spatial clustering with smart strategies inspired by nature to get better groups. They mix a specific spatial fuzzy clustering method with seven nature-inspired optimization techniques, tweaking some parts to improve results. Their package also offers different ways to measure distance, helping it work better in various spatial cases.
What this means in practice
- •For geospatial analysts: Improve clustering of spatial data by combining fuzzy methods with nature-inspired algorithms for better pattern detection in maps and geographic studies.
- •For environmental data teams: Enhance spatial grouping of environmental measurements using hybrid fuzzy clustering to reveal subtle spatial patterns that standard methods might miss.
Authors
Bahrul Ilmi Nasution, Rezzy Eko Caraka, Robert Kurniawan
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.