A Novel Spatial Clustering with Obstacles Constraints Based on PNPSO and K-Medoids

被引：0

作者：

Zhang, Xueping ^{[1
]}

Du, Haohua ^{[2
]}

Yang, Tengfei ^{[1
]}

Zhao, Guangcai ^{[1
]}

机构：

[1] Henan Univ Technol, Sch Informat Sci & Engn, Zhengzhou 450052, Peoples R China

[2] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China

来源：

ADVANCES IN SWARM INTELLIGENCE, PT 2, PROCEEDINGS | 2010年 / 6146卷

关键词：

Spatial Clustering; Obstacles Constraints; Particle Swarm Optimization; Piecewise Linear Chaotic Map; Nonlinear Inertia Weights; K-Medoids;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

引用

页码：476 / +

页数：3

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