A novel spatial clustering algorithm with sampling

Abstract

Spatial clustering is one of the very important spatial data mining techniques. So far, a lot of spatial clustering algorithms have been proposed. DBSCAN is one of the effective spatial clustering algorithms, which can discover clusters of any arbitrary shape and handle the noise effectively. However, it has also several disadvantages. First, it does based on only spatial attributes, does not consider non-spatial attributes in spatial databases. Secondly, when DBSCAN does handle large-scale spatial databases, it requires large volume of memory support and the I/O cost. In this paper, a novel spatial clustering algorithm with sampling (NSCAS) based on DBSCAN is developed, which not only clusters large-scale spatial databases effectively, but also considers spatial attributes and non-spatial attributes. Experimental results of 2-D spatial datasets show that NSCAS is feasible and efficient. © Springer-Verlag Berlin Heidelberg 2007.