A novel spatial clustering with obstacles constraints based on PNPSO and k-medoids

Authors:
Xueping Zhang;Haohua Du;Tengfei Yang;Guangcai Zhao
Affiliations:
School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China;School of computer science and engineering, Beihang University, Beijing, China;School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China;School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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Abstract

In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.