Using Greedy algorithm: DBSCAN revisited II

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Esteret al., 1996), and has the following advantages: first, Greedy algorithm substitutes forR ^*-tree (Bechmannet al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency. Project (No. 2002AA2010) supported by the Hi-Tech Research and Development Program (863) of China