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Using Greedy algorithm: DBSCAN revisited II.

Shi-hong Yue·Ping Li·Ji-dong Guo·Shui-geng Zhou
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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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AlgorithmsArtificial IntelligenceCluster AnalysisDatabase Management SystemsDatabases, FactualInformation Storage and RetrievalPattern Recognition, AutomatedSoftwareEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov't

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Shi-hong Yue, Ping Li, Ji-dong Guo, Shui-geng Zhou. 2004. Using Greedy algorithm: DBSCAN revisited II.. https://doi.org/10.1631/jzus.2004.1405

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