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Encyclopedia of Machine Learning and Data Mining pp 349–353Cite as

Density-Based Clustering

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Abstract

The chapter gives a concise explanation of the basic principles of density-based clustering and points out important ”milestone papers” in this area.

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Recommended Reading

  • Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: Delis A, Faloutsos C, Ghandeharizadeh S (eds) Proceedings of the 1999 ACM SIGMOD international conference on management of data, Philadelphia

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  • Campello RJGB, Moulavi D, Sander J (2013) Density-based clustering based on hierarchical density estimates. In: Proceedings of the 17th Pacific-Asia conference on knowledge discovery in databases, PAKDD 2013. Lecture notes in computer science, vol 7819, p 160

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  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis E, Han J, Fayyad UM (eds) Proceedings of the 2nd international conference on knowledge discovery and data mining, Portland

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  • Hartigan JA (1975) Clustering algorithms. Wiley, New York

    MATH  Google Scholar 

  • Hinneburg A, Keim DA (1998) An efficient approach to clustering in large multimedia databases with noise. In: Agrawal R, Stolorz P (eds) Proceedings of the 4th international conference on knowledge discovery and data mining, New York City

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  • Sander J, Ester M, Kriegel H-P, Xu X (1998) Density-Based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Discov 2(2):169–194

    Article  Google Scholar 

  • Stuetzle W (2003) Estimating the cluster tree of a density by analyzing the minimal spanning tree of a sample. J Classif 20(1):025–047

    Article  MathSciNet  MATH  Google Scholar 

  • Wishart D (1969) Mode analysis: a generalization of nearest neighbor which reduces chaining effects. In: Numerical Taxonomy, ed. A. J. Cole, London: Academic Press, 282–311

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Alberta, Canada Statistical Machine Learning Group, NICTA, Canberra, ACT, Edmonton, AB, Australia

    Joerg Sander

Authors
  1. Joerg Sander
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Corresponding author

Correspondence to Joerg Sander .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Sander, J. (2017). Density-Based Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_70

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