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Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids


Xueping Zhang, Jiayao Wang, Fang Wu


Vol. 6  No. 10  pp. 109-114


Spatial clustering has been an active research area in Spatial Data Mining (SDM). Many methods on spatial clustering 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. 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. The GKSCOC 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 GKSCOC algorithm performs better than the IKSCOC algorithm in terms of quantization error


Spatial Clustering, Genetic Algorithms, K-Medoids Algorithm, Obstacles Constraints