@inproceedings{d7a5b2b66acd4b809f7c34858d23811b,
title = "A quantum particle swarm optimization used for spatial clustering with obstacles constraints",
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.",
keywords = "Obstacles constraints, Quantum particle swarm optimization, Spatial clustering, Spatial obstructed distance",
author = "Xueping Zhang and Jiayao Wang and Haohua Du and Tengfei Yang and Yawei Liu",
year = "2009",
doi = "10.1007/978-3-642-04020-7\_45",
language = "英语",
isbn = "3642040195",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "424--433",
booktitle = "Emerging Intelligent Computing Technology and Applications",
address = "德国",
note = "5th International Conference on Intelligent Computing, ICIC 2009 ; Conference date: 16-09-2009 Through 19-09-2009",
}