Using Greedy algorithm: DBSCAN revisited II
Using Greedy algorithm: DBSCAN revisited II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.