PSO spatial clustering with obstacles constraints
DU Zhen-fang · Jisuanji gongcheng yu sheji · 2007
Clustering spatial data is one of the main methods in the spatial data mining (SDM). Classical clustering algorithms have ignored the fact that many constraints exit in the real world and could affect the correctness of clustering result. After analyzing the disadvantages of the classical K-medoids clustering algorithm, a novel K-medoids clustering algorithm based on particle swarm optimization algorithm with obstacles constraints is proposed. The classical K-medoids algorithm exits the local optima and is sensitive to initialization. The experimental results show that this novel clustering method not only has greater searching capability, but also has fast convergent rate.