Abstract
In this paper, we propose an Improved Ant Colony Optimization (IACO) and Hybrid Particle Swarm Optimization (HPSO) method for Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use IACO to obtain the shortest obstructed distance, and then we develop a novel HPKSCOC based on HPSO and K-Medoids to cluster spatial data with obstacles. The experimental results demonstrate that the proposed method, performs better than Improved K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.
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© 2008 Springer-Verlag Berlin Heidelberg
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Zhang, X., Wang, J., Zhang, D., Fan, Z. (2008). An IACO and HPSO Method for Spatial Clustering with Obstacles Constraints. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_102
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DOI: https://doi.org/10.1007/978-3-540-85984-0_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
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