Clustering Spatial Data with Obstacles Using Improved Ant Colony Optimization and Hybrid Particle Swarm Optimization

Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM). In this paper, we propose an improved ant colony optimization (IACO) and hybrid particle swarm optimization (HPSO) method for SCOC. In the process of doing so, we first use IACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel HPKSCOC based on HPSO and K-Medoids to cluster spatial data with obstacles, which can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints.