DocumentCode :
2795381
Title :
A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids
Author :
Zhang, Xueping ; Wang, Jiayao ; Wu, Fang ; Fan, Zhongshan ; Li, Xiaoqing
Author_Institution :
Comput. Sci. & Eng., Henan Univ. of Technol.
Volume :
1
fYear :
2006
fDate :
16-18 Oct. 2006
Firstpage :
605
Lastpage :
610
Abstract :
Spatial clustering is an important research topic in spatial data mining (SDM). Many methods have been proposed in the literature, but few of them have taken into account constraints that may be present in the data or constraints on the clustering. These constraints have significant influence on the results of the clustering process of large spatial data. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on genetic algorithms (GAs) and K-Medoids, called GKSCOC, which aims to cluster spatial data with obstacles constraints. It 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 it is better than standard GAs and K-Medoids
Keywords :
constraint handling; data mining; genetic algorithms; pattern clustering; GKSCOC; K-Medoids; genetic algorithms; large spatial data; obstacles constraints; spatial clustering; spatial data mining; Bridges; Clustering algorithms; Clustering methods; Data engineering; Data mining; Genetic algorithms; Genetic engineering; Partitioning algorithms; Rivers; Road transportation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
Type :
conf
DOI :
10.1109/ISDA.2006.75
Filename :
4021508
Link To Document :
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