title: Using Greedy algorithm: DBSCAN revisited II.
abstract: The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R(*)-tree (Bechmann et 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.
authors.affiliation: Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China; Shyue@iipc.zju.edu.cn.
authors.forename: Shi-hong
authors.initials: SH
authors.lastname: Yue
authors.forename: Ping
authors.initials: P
authors.lastname: Li
authors.forename: Ji-dong
authors.initials: JD
authors.lastname: Guo
authors.forename: Shui-geng
authors.initials: SG
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003628:Database Management Systems; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software