Clustering Spatial Data with Obstacles Using Improved Ant Colony Optimization and Hybrid Particle Swarm Optimization

被引：1

作者：

Zhang, Xueping ^{[1
]}

Zhang, Qingzhou ^{[1
]}

Fan, Zhongshan ^{[2
]}

Deng, Gaofeng ^{[1
]}

Zhang, Chuang ^{[1
]}

机构：

[1] Henan Univ Technol, Comp Sci & Engn, Zhengzhou 450001, Henan, Peoples R China

[2] Henan Acad Traff Sci & Technol, Zhengzhou 450052, Henan, Peoples R China

来源：

FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/FSKD.2008.128

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spatial Clustering with Obstacles Constraints (SCOC) has been a new topic in Spatial Data Mining (SDM). In this paper, we propose an Improved Ant Colony Optimization (IACO) and Hybrid Particle Swarm Optimization (HPSO) method for SCOC In the process of doing so, we first use IACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel HPKSCOC based on HPSO and K-Medoids to cluster spatial data with obstacles, which can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints.

引用

页码：424 / +

页数：2

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