  Clustering analysis has received considerable attention in spatial data
mining for several years. With the rapid development of the geospatial
information technologies, the size of spatial information data is growing
exponentially 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 on
MapReduce for clustering massive spatial data is proposed. The initialization
algorithm to decrease the number of iterations is combined with the MapReduce
framework. Comparative Experiments conducted over different dataset and
different number of nodes indicate that the proposed K-Medoids spatial
clustering algorithm provides better efficiency than traditional K-Medoids and
scales well while processing massive spatial data on commodity hardware.
