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...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Agrawal R., Gehrke J., Gunopulos D., and Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1998, pp. 94–105.
Ankerst M., Breunig M.M., Kriegel H-P., and Sander J. OPTICS: Ordering Points To Identify the Clustering Structure. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999, pp. 49–60.
Cao F., Ester M., Qian W., and Zhou A. Density-based clustering over an evolving data stream with noise. In Proc. SIAM Conf. on Data Mining, 2006.
Ester M., Kriegel H-P., Sander J., Wimmer M., and Xu X. Incremental Clustering for Mining in a Data Warehousing Environment. In Proc. 24th Int. Conf. on Very Large Data Bases, 1998, pp. 323–333.
Ester M., Kriegel H-P., Sander J., and Xu X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, 1996, pp. 226–231.
Hinneburg A. and Keim D.A. An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, 1998, pp. 58–65.
Hinneburg A. and Keim D.A. Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering. In Proc. 25th Int. Conf. on Very Large Data Bases, 1999, pp. 506–517.
Sander J., Ester M., Kriegel H-P., and Xu X. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Discov., 2(2):169–194, 1998.
Sheikholeslami G., Chatterjee S., and Zhang A. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proc. 24th Int. Conf. on Very Large Data Bases, 1998, pp. 428–439.
Xu X., Yuruk N., Feng Z., Thomas A., and Schweiger J. SCAN: a structural clustering algorithm for networks. In Proc. 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2007, pp. 824–833.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Ester, M. (2009). Density-based Clustering. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_605
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_605
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering