Spatial clustering with obstacles constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO

Authors:
Xueping Zhang;Haohua Du;Jiayao Wang
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
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2010

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Abstract

Spatial clustering with constraints has been a new topic in spatial data mining. A novel Spatial Clustering with Obstacles Constraints (SCOC) by dynamic piecewise-mapped and nonlinear inertia weights particle swarm optimization is proposed in this paper. The experiments show that the 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 PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.