A Novel Spatial Obstructed Distance Using Quantum-Behaved Particle Swarm Optimization

摘要：

Spatial Clustering with Obstacles Constraints (SCOC) has been a new topic in Spatial Data Mining (SDM). Spatial Obstructed Distance (SOD) is the key to SCOC.The obstacles constraint is generally ignored in computing distance between two points,and it leads to the clustering result which is of no value,so obstructed distance has a great effect upon clustering result.In this paper,we propose a novel Spatial Obstructed Distance using Quantum-Behaved Particle Swarm Optimization (QPSO) based on Grid model to obtain obstructed distance, which is named QPGSOD.The experimental results show that QPGSOD is effective,and it can not only give attention to higher local constringency speed and stronger global optimum search.

关键词： Spatial Obstructed Distance Particle Swarm Optimization Quantum-Behaved Grid model

作者: Xueping Zhang Hong Yi Dan Cao Yawei Liu Tengfei Yang

作者单位: Sehooi of Information Science & Engineering Henan University of Technology Zhengzhou, China Key Labo Sehooi of Information Science & Engineering Henan University of Technology Zhengzhou, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 233-236

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）