Article

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

Authors:

Martin Ester,

Hans-Peter Kriegel,

Jörg Sander, and

Xiaowei XuAuthors Info & Claims

KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining

August 1996

Pages 226 - 231

Published: 02 August 1996 Publication History

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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.

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Index Terms

A density-based algorithm for discovering clusters in large spatial databases with noise
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering
    2. Spatial-temporal systems

Index terms have been assigned to the content through auto-classification.

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KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining

August 1996

387 pages

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AAAI Press

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Published: 02 August 1996

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Wu NLi YYang HChen HDai SHao CYu CXie Y(2024)Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and ProspectACM Transactions on Design Automation of Electronic Systems10.1145/366130829:4(1-42)Online publication date: 24-Apr-2024
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Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

A local-density based spatial clustering algorithm with noise

A New Density-Based Scheme for Clustering Based on Genetic Algorithm

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