• 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={KDD},
  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…Expand
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Abstract—clustering Algorithms Are Attractive for the Task Of
class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to
Abstract—clustering Algorithms Are Attractive for the Task Of
class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to
Abstract—clustering Algorithms Are Attractive for the Task Of
class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to
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