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  • Title: Using Greedy algorithm: DBSCAN revisited II.
    Author: Yue SH, Li P, Guo JD, Zhou SG.
    Journal: J Zhejiang Univ Sci; 2004 Nov; 5(11):1405-12. PubMed ID: 15495334.
    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.
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