Using Greedy algorithm: DBSCAN revisited II

作     者：岳士弘 李平 郭继东 周水庚 

作者机构：Institute of Industrial Process Control Yining 835000 Hangzhou 310027 China Zhejiang University Yili Teacher’s College 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报A辑(应用物理与工程)(英文版))

年 卷 期：2004年第11期

页      面：94-101页

核心收录：

学科分类：08[工学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project (No. 2002AA2010) supported by the Hi-Tech Research and Development Program (863) of China 

主　　题：DBSCAN algorithm Greedy algorithm Density-skewed cluster 

摘      要：The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clus- tering 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 arbi- trary-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.