A novel spatial clustering with obstacles constraints based on particle swarm optimization and K-medoids

Authors:
Xueping Zhang;Jiayao Wang;Mingguang Wu;Yi Cheng
Affiliations:
School of Information Science and Eng., Henan Univ. of Techn., Zhengzhou, China and School of Surveying and Mapping, PLA Information Eng. Univ., Zhengzhou, China and Geomatics and Applications Lab ...;School of Surveying and Mapping, PLA Information Engineering University, Zhengzhou, China;School of Surveying and Mapping, PLA Information Engineering University, Zhengzhou, China;School of Surveying and Mapping, PLA Information Engineering University, Zhengzhou, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on PSO and K-Medoids, called PKSCOC, which aims to cluster spatial data with obstacles constraints. The PKSCOC algorithm 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. The results on real datasets show that the PKSCOC algorithm performs better than the IKSCOC algorithm in terms of quantization error.