A Novel Spatial Clustering with Obstacles Constraints Based on Particle Swarm Optimization and K-Medoids.

In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on PSO and K-Medoids, called PKSCOC, which aims to cluster spatial data with obstacles constraints. The PKSCOC algorithm 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. The results on real datasets show that the PKSCOC algorithm performs better than the IKSCOC algorithm in terms of quantization error. [ABSTRACT FROM AUTHOR]