• Corpus ID: 355163

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

@inproceedings{Ester1996ADA,
  title={A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise},
  author={Martin Ester and Hans-Peter Kriegel and J{\"o}rg Sander and Xiaowei Xu},
  booktitle={Knowledge Discovery and Data Mining},
  year={1996}
}
Clustering algorithms are attractive for the task of class identification in spatial databases. [] Key Method DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the…

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