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 toExpand
    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 toExpand
      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 toExpand
        Multi Density DBSCAN
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        The DBSCAN clustering algorithm is extended so that it can also detect clusters that differ in densities and without the need to input the value of Eps because the algorithm can find the appropriate value for each cluster individually by replacing Eps by Local cluster density. Expand
        Density Clustering Algorithm Based on Radius of Data (DCBRD)
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        A density based clustering algorithm is presented relying on a knowledge acquired from the data which is designed to discover clusters of arbitrary shape and size and which requires no input parameter. Expand
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        • B. Liu
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        • 2006 International Conference on Machine Learning and Cybernetics
        • 2006
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