Spatial clustering with obstacles constraints using PSO-DV and K-Medoids

Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM).In this paper, we propose an advanced Particle swarm optimization (PSO) and differential evolution (DE) method for SCOC. In the process of doing so,we first developed a novel spatial obstructed distance using PSO-DV(particle swarm optimization with differentially perturbed Velocity) based on grid model to obtain obstructed distance, which is named PDGSOD, and then we presented a new PDKSCOC based on PSO-DV and K-Medoids to cluster spatial data with obstacles constraints. The experimental results show that PDGSOD is effective, and PDKSCOC can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering; and it performs better than Improved KMedoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).