Latent variable based spatial clustering with background noise

We propose a finite mixture model for clustering of the spatial data patterns. The model is based on the spatial distances between the data locations in such a way that both the distances of the points to the cluster centers and the distances of a given point to its neighbors within a defined window are involved in the model. Nevertheless, we take into consideration the background noise as well in the model. We test the algorithm on some simulated data sets with different background noise levels.