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Encyclopedia of Machine Learning pp 270–273Cite as

Density-Based Clustering

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Synonyms

Estimation of density level sets; Mode analysis; Non-parametric cluster analysis

Definition

Density-Based Clustering refers to  unsupervised learning methods that identify distinctive groups/clusters in the data, based on the idea that a cluster in a data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density. The data points in the separating regions of low point density are typically considered noise/outliers.

Motivation and Background

Clustering in general is an unsupervised learning task that aims at finding distinct groups in data, called clusters. The minimum requirements for this task are that the data is given as some set of objects O for which a dissimilarity-distance function d : O × O → R + is given. Often, O is a set of d-dimensional real valued points, O ⊂ R d, which can be viewed as a sample from some unknown probability density p(x), with d as the Euclidean or some other form of distance.

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Recommended Reading

  • Ankerst, M., Breunig, M. M., Kriegel, H.-P., Sander, J. (1999). OPTICS: Ordering Points to Identify the Clustering Structure. In A. Delis, C. Faloutsos, S. Ghandeharizadeh (Eds.), Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data. Philadelphia: ACM.

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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 E. Simoudis, J. Han, & U.M. Fayyad (Eds.), Proceedings of the second International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press.

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  • Hartigan, J. A. (1975). Clustering Algorithms. New York: Wiley.

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  • Hinneburg, A., Keim, D. A. (1998). En Efficient Approach to Clustering in Large Multimedia Databases with Noise. In R. Agrawal, & P. Stolorz (Eds.), Proceedings of the fourth International Conference on Knowledge Discovery and Data Mining. New York City: AAAI Press.

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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 Mining and Knowledge Discovery, 2(2), 169–194.

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  • Stuetzle, W. (2003). Estimating the Cluster Tree of a Density by Analyzing the Minimal Spanning Tree of a Sample. Journal of Classification, 20(1), 025–047.

    MathSciNet  Google Scholar 

  • Wishart, D. (1969). Mode analysis: A generalization of nearest neighbor which reduces chaining effects. In A. J. Cole (Ed.), Proceedings of the Colloquium in Numerical Taxonomy. Scotland: St. Andrews.

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Authors and Affiliations

    Authors
    1. Joerg Sander
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    Editor information

    Editors and Affiliations

    1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2052

      Claude Sammut

    2. Faculty of Information Technology, Clayton School of Information Technology, Monash University, P.O. Box 63, Victoria, Australia, 3800

      Geoffrey I. Webb

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    © 2011 Springer Science+Business Media, LLC

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    Sander, J. (2011). Density-Based Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_211

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