A quantum particle swarm optimization used for spatial clustering with obstacles constraints

Authors:
Xueping Zhang;Jiayao Wang;Haohua Du;Tengfei Yang;Yawei Liu
Affiliations:
School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China and Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou Uni ...;School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China and School of Surveying and Mapping, PLA Information Engineering University, 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:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

In this paper, a more effective Quantum Particle Swarm Optimization (QPSO) method for Spatial Clustering with Obstacles Constraints (SCOC) is presented. In the process of doing so, we first proposed a novel Spatial Obstructed Distance using QPSO based on Grid model (QPGSOD) to obtain obstructed distance, and then we developed a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles constraints. The contrastive experiments show that QPGSOD is effective, and QPKSCOC 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.