Title :
Spatial Clustering with Obstacles Constraints by Ant Colony Optimization and Quantum Particle Swarm Optimization
Author :
Zhang, Xueping ; Wu, Jianjun ; Si, Haifang ; Yang, Tengfei ; Liu, Yawei
Author_Institution :
Sch. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China
Abstract :
The paper proposed a novel ant colony optimization (ACO) and quantum particle swarm optimization (QPSO) method for spatial clustering with obstacles constraints (SCOC). We first developed AQPGSOD using ACO and QPSO based on grid model to obtain obstructed distance, and then we presented a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles. The experimental results show that AQPGSOD 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.
Keywords :
particle swarm optimisation; pattern clustering; K-Medoids; ant colony optimization; obstacles constraints; quantum particle swarm optimization; spatial data clustering; Ant colony optimization; Artificial intelligence; Clustering algorithms; Computational intelligence; Data engineering; Data mining; Educational technology; Information science; Particle swarm optimization; Quantum computing; Ant Colony Optimization; Obstacles Constraints; Obstructed Distance; Quantum Particle Swarm Optimization; Spatial Clustering;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.166
