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Clustering analysis has received considerable attention in spatial datamining for several years. With the rapid development of the geospatialinformation technologies, the size of spatial information data is growingexponentially which makes clustering massive spatial data a challenging task.In order to improve the efficiency of spatial clustering for large scale data,many researchers proposed several efficient clustering algorithms in parallel.In this paper, a new K-Medoids++ spatial clustering algorithm based onMapReduce for clustering massive spatial data is proposed. The initializationalgorithm to decrease the number of iterations is combined with the MapReduceframework. Comparative Experiments conducted over different dataset anddifferent number of nodes indicate that the proposed K-Medoids spatialclustering algorithm provides better efficiency than traditional K-Medoids andscales well while processing massive spatial data on commodity hardware.

Parallel K-Medoids++ Spatial Clustering Algorithm Based on MapReduce