Spatial Clustering with Obstacles Constraints by Hybrid Particle Swarm Optimization with GA Mutation

Authors:
Xueping Zhang;Hui Yin;Hongmei Zhang;Zhongshan Fan
Affiliations:
School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China 450001;School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China 450001;School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China 450001;Henan Academy of Traffic Science and Technology, Zhengzhou, China 450052
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) by an advanced Hybrid Particle Swarm Optimization (HPSO) with GA mutation. In the process of doing so, we first use HPSO to get obstructed distance, and then we developed a novel HPKSCOC based on HPSO and K-Medoids to cluster spatial data with obstacles constraints. The experimental results show that the HPKSCOC 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; and it performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).