Nature-Inspired Spatial Clustering
Fuzzy Geographicaly Weighted Clustering with Artificial Bee Colony Opt...
Fuzzy Geographicaly Weighted Clustering
Classical Fuzzy Geographicaly Weighted Clustering
Fuzzy Geographicaly Weighted Clustering with Flower Pollination Algori...
Fuzzy Geographicaly Weighted Clustering with Gravitational Search Algo...
Fuzzy Geographicaly Weighted Clustering with Harris-Hawk Optimization
Fuzzy Geographicaly Weighted Clustering with (Intelligent) Firefly Alg...
Fuzzy Geographicaly Weighted Clustering with Particle Swarm Optimizati...
Fuzzy Geographicaly Weighted Clustering with Teaching - Learning Based...
Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.