Abstract
Spatial clustering is an important research topic in Spatial Data Mining (SDM). In this paper, we propose a particle swarm optimization (PSO) method for Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use PSO algorithm via MAKLINK graphic to get the optimal obstructed path, and then we developed PSO K-Medoids SCOC (PKSCOC) algorithm to cluster spatial data with obstacles constraints. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which 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.
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Zhang, X., Wang, J., Fan, Z., Li, X. (2007). A Particle Swarm Optimization Method for Spatial Clustering with Obstacles Constraints. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_41
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DOI: https://doi.org/10.1007/978-3-540-74205-0_41
Publisher Name: Springer, Berlin, Heidelberg
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