Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Density-Based Clustering

  • Martin Ester
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_605

Definition

Density-based clusters are dense areas in the data space separated from each other by sparser areas. Furthermore, the density within the areas of noise is lower than the density in any of the clusters. Formalizing this intuition, for each core point the neighborhood of radius Eps has to contain at least MinPts points, i.e., the density in the neighborhood has to exceed some threshold. A point q is directly-density-reachable from a core point p if q is within the Eps-neighborhood of p, and density-reachability is given by the transitive closure of direct density-reachability. Two points p and q are called density-connected if there is a third point o from which both p and q are density-reachable. A cluster is then a set of density-connected points which is maximal with respect to density-reachability. Noiseis defined as the set of points in the database not belonging to any of its clusters. The task of density-based clustering is to find all clusters with respect to...

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Recommended Reading

  1. 1.
    Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1998. p. 94–105.Google Scholar
  2. 2.
    Ankerst M, Breunig MM, Kriegel H-P, Sander J. OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999. p. 49–60.Google Scholar
  3. 3.
    Cao F, Ester M, Qian W, Zhou A. Density-based clustering over an evolving data stream with noise. In: Proceedings of the SIAM Conference on Data Mining; 2006.Google Scholar
  4. 4.
    Ester M, Kriegel H-P, Sander J, Wimmer M, Xu X. Incremental clustering for mining in a data warehousing environment. In: Proceedings of the 24th International Conference on Very Large Data Bases; 1998. p. 323–33.Google Scholar
  5. 5.
    Ester M, Kriegel H-P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining; 1996. p. 226–31.Google Scholar
  6. 6.
    Hinneburg A, Keim DA. An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining; 1998. p. 58–65.Google Scholar
  7. 7.
    Hinneburg A, Keim DA. Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering. In: Proceedings of the 25th International Conference on Very Large Data Bases; 1999. p. 506–17.Google Scholar
  8. 8.
    Sander J, Ester M, Kriegel H-P, Xu X. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Discov. 1998;2(2):169–94.CrossRefGoogle Scholar
  9. 9.
    Sheikholeslami G, Chatterjee S, Zhang A. Wave Cluster: a multi-resolution clustering approach for very large spatial databases. In: Proceedings of the 24th International Conference on Very Large Data Bases; 1998. p. 428–39.Google Scholar
  10. 10.
    Xu X, Yuruk N, Feng Z, Thomas A, Schweiger J. SCAN: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2007. p. 824–33.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Simon Fraser UniversityBurnabyCanada

Section editors and affiliations

  • Dimitrios Gunopulos
    • 1
  1. 1.Department of Computer Science and EngineeringThe University of California at Riverside, Bourns College of EngineeringRiversideUSA