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

Volume

1

fYear

2009

fDate

7-8 Nov. 2009

Firstpage

154

Lastpage

158

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/AICI.2009.166

Filename

5376063