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A density-based algorithm for discovering clusters in large spatial databases with noise (1996)

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by Martin Ester , Hans-Peter Kriegel , Jörg Sander , Xiaowei Xu
Citations:1785 - 70 self
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BibTeX

@INPROCEEDINGS{Ester96adensity-based,
    author = {Martin Ester and Hans-Peter Kriegel and Jörg Sander and Xiaowei Xu},
    title = {A density-based algorithm for discovering clusters in large spatial databases with noise},
    booktitle = {},
    year = {1996},
    pages = {226--231},
    publisher = {AAAI Press}
}

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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 determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. 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 well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

Keyphrases

large spatial database    arbitrary shape    density-based algorithm    input parameter    domain knowledge    synthetic data    class identification    density-based notion    appropriate value    real data    spatial database    following requirement    well-known clustering algorithm    well-known algorithm clar-ans    algorithm dbscan relying    experimental evaluation    minimal requirement    good efficiency    large database    dbscan outperforms clarans   

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