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A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets

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Advances in Artificial Intelligence (Canadian AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3501))

Abstract

Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we briefly describe the methods and then compare them from a theoretical view. Finally, we give an empirical comparison of the algorithms.

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Xin Wang & Howard J. Hamilton

Authors
  1. Xin Wang
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  2. Howard J. Hamilton
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Editors and Affiliations

  1. Département d’informatique et de recherche opérationelle, CP 6128 succ. Centre-Ville, Université de Montréal, H3C 3J7, Montréal, Canada

    Balázs Kégl

  2. Département d’informatique et de recherche opérationnelle, Université de Montréal,  

    Guy Lapalme

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Wang, X., Hamilton, H.J. (2005). A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_14

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  • DOI: https://doi.org/10.1007/11424918_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25864-3

  • Online ISBN: 978-3-540-31952-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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