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).
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Zhang, X., Yin, H., Zhang, H., Fan, Z. (2008). Spatial Clustering with Obstacles Constraints by Hybrid Particle Swarm Optimization with GA Mutation. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_64
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DOI: https://doi.org/10.1007/978-3-540-87732-5_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
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