A Novel Spatial Obstructed Distance Using Quantum-Behaved Particle Swarm Optimization

Spatial clustering with obstacles constraints (SCOC) has been a new topic in spatial data mining (SDM). Spatial obstructed distance (SOD) is the key to SCOC. The obstacles constraint is generally ignored in computing distance between two points, and it leads to the clustering result which is of no value, so obstructed distance has a great effect upon clustering result. In this paper, we propose a novel spatial obstructed distance using quantum-behaved particle swarm optimization (QPSO) based on grid model to obtain obstructed distance, which is named QPGSOD. The experimental results show that QPGSOD is effective, and it can not only give attention to higher local constringency speed and stronger global optimum search.