Hybrid Particle Swarm Optimization with GA Mutation to Solve Spatial Clustering with Obstacles Constraints

Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM). In this paper, we propose an advanced hybrid particle swarm optimization (HPSO) with GA mutation for SCOC. In the process of doing so, we first use HPSO to get obstructed distance, and then we developed a novel HPKSCOC based on HPSO and K-Medoids to cluster spatial data with obstacles constraints. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).