Research Article
Spatial Clustering with Obstacles Constraints Using Particle Swarm Optimization
@INPROCEEDINGS{10.4108/infoscale.2007.206, author={Xueping Zhang and Jiayao Wang and Hongmei Zhang and Jianzhong Guo and Xiaoqing Li}, title={Spatial Clustering with Obstacles Constraints Using Particle Swarm Optimization}, proceedings={2nd International ICST Conference on Scalable Information Systems}, proceedings_a={INFOSCALE}, year={2010}, month={5}, keywords={Spatial Clustering Obstacles Constraints MAKLINK Graph Obstructed Distance Particle Swarm Optimization.}, doi={10.4108/infoscale.2007.206} }- Xueping Zhang
Jiayao Wang
Hongmei Zhang
Jianzhong Guo
Xiaoqing Li
Year: 2010
Spatial Clustering with Obstacles Constraints Using Particle Swarm Optimization
INFOSCALE
ICST
DOI: 10.4108/infoscale.2007.206
Abstract
Spatial clustering is an important research topic in Spatial Data Mining (SDM). Although many methods have been proposed in the literature, very few have taken into account constraints that may be present in the data or constraints on the clustering. These constraints have significant influence on the results of the clustering process of large spatial data. In this paper, we propose a particle swarm optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). We first use the PSO algorithm based MAKLINK graph to obtain the best obstructed path and then propose a novel PSO and K-Medoids method for SCOC, which is called PKSCOC, 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 experimental results show that the PKSCOC algorithm is better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher convergence speed than Genetic K-Medoids SCOC (GKSCOC).
