Genetic K-Medoids Spatial Clustering with Obstacles Constraints

Spatial clustering is an important research topic in spatial data mining (SDM). It is not only an important effective method but also a prelude of other task for SDM. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. So, 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 consider the obstacles constraints and make the results of spatial clustering more practice. Its performance has compared to GAs, K-Medoids; and the results on real datasets show that it is better than standard GAs and K-Medoids. The drawback of this method is a comparatively slower speed in spatial clustering