Using IACO and QPSO to solve spatial clustering with obstacles constraints

Spatial clustering has been an active research area in the data mining community. Spatial clustering is not only an important effective method but also a prelude of other task for spatial data mining (SDM). In this paper, we propose an improved ant colony optimization (IACO) and quantum particle swarm optimization (QPSO) method for spatial clustering with obstacles constraints (SCOC). In the process of doing so, we first use IACO to obtain the shortest obstructed distance, and then we develop a novel QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles. The experimental results demonstrate that the proposed method, performs better than Improved K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.