Fast Spatial Clustering with Different Metrics and in the Presence of Obstacles Vladimir Estivill-Castro and Ickjai Lee   View Paper (PDF)   Return to Spatial Database Clustering Abstract proposed an effective and efficient boundary-based clustering method overcoming drawbacks of traditional spatial clustering, but has a geometric focus. By factoring out the topological aspects of the method we obtain a generic boundary-based clustering that robustly generalizes for arbitrary Minkowski distances and is capable of handling obstacles. We illustrate this with the Manhattan distance and the Dominance distance. Experiments demonstrate that our method consistently finds various types of high-quality clusters within subquadratic time. DiSC'02 © 2003 Association for Computing Machinery