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

Ester, M; Kriegel, H P; Sander, J; Xiaowei, Xu

Title: A density-based algorithm for discovering clusters in large spatial databases with noise

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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. DB SCAN 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 CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

Authors:

Ester, M; Kriegel, H P; Sander, J; Xiaowei, Xu ^[1]

Univ. of Munich (Germany)

Publication Date:: 1996-12-31

OSTI Identifier:: 421283

Report Number(s):: CONF-960830-
TRN: 96:005928-0038

Resource Type:: Conference

Resource Relation:: Conference: 2. international conference on knowledge discovery and data mining, Portland, OR (United States), 2-4 Aug 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the second international conference on knowledge discovery & data mining; Simoudis, E.; Han, J.; Fayyad, U. [eds.]; PB: 405 p.

Country of Publication:: United States

Language:: English

Subject:: 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; DATA BASE MANAGEMENT; KNOWLEDGE BASE; INFORMATION RETRIEVAL; IMAGES; ALGORITHMS; ARTIFICIAL INTELLIGENCE; EFFICIENCY; NOISE

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                    Ester, M, Kriegel, H P, Sander, J, and Xiaowei, Xu. A density-based algorithm for discovering clusters in large spatial databases with noise.  United States: N. p., 1996. 
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                    Ester, M, Kriegel, H P, Sander, J, & Xiaowei, Xu. A density-based algorithm for discovering clusters in large spatial databases with noise.  United States.

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                    Ester, M, Kriegel, H P, Sander, J, and Xiaowei, Xu. 1996.  
        "A density-based algorithm for discovering clusters in large spatial databases with noise".  United States.

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@article{osti_421283,

  title        = {A density-based algorithm for discovering clusters in large spatial databases with noise},

  author       = {Ester, M and Kriegel, H P and Sander, J and Xiaowei, Xu},

  abstractNote = {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. DB SCAN 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 CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/421283},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {1996},

  month        = {12}

}

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