An adaptive spatial clustering (ASC) algorithm is proposed that employs sweep-circle techniques and a dynamic threshold setting based on Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of priori knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering large data sets.