Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media.
Spatial clustering is one of the very important spatial data mining techniques. So far, a lot of spatial clustering algorithms
have been proposed. In this paper we propose a robust spatial clustering algorithm named SCABDT (Spatial Clustering
Algorithm Based on Delaunay Triangulation). SCABDT demonstrates important advantages over the previous works.
First, it discovers even arbitrary shape of cluster distribution. Second, in order to execute SCABDT, we do not need to
know any priori nature of distribution. Third, like DBSCAN, Experiments show that SCABDT does not require so much
CPU processing time. Finally it handles efficiently outliers.
It is known that digital elevation models (DEMs) can vary in quality depending on spatial sampling schemes. Three DEMs were created by Kriging interpolation from sampling points which come from different sampling schemes. Using a test setup, three different sampling schemes and different semivariogram models (Circular, Spherical, Tetraspherical, Pentaspherical, Exponential, Gaussian, Rational Quadratic, Hole Effect, K-bessel, J-bessel, and Stable) are compared.
The results show uniform random sampling scheme to better than the other candidates.