Locally Scaled Density Based Clustering

  • Ergun Biçici
  • Deniz Yuret
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)


Density based clustering methods allow the identification of arbitrary, not necessarily convex regions of data points that are densely populated. The number of clusters does not need to be specified beforehand; a cluster is defined to be a connected region that exceeds a given density threshold. This paper introduces the notion of local scaling in density based clustering, which determines the density threshold based on the local statistics of the data. The local maxima of density are discovered using a k-nearest-neighbor density estimation and used as centers of potential clusters. Each cluster is grown until the density falls below a pre-specified ratio of the center point’s density. The resulting clustering technique is able to identify clusters of arbitrary shape on noisy backgrounds that contain significant density gradients. The focus of this paper is to automate the process of clustering by making use of the local density information for arbitrarily sized, shaped, located, and numbered clusters. The performance of the new algorithm is promising as it is demonstrated on a number of synthetic datasets and images for a wide range of its parameters.


Spectral Cluster Synthetic Dataset Density Threshold Core Point Density Base Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231 (1996)Google Scholar
  2. 2.
    Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: Optics: ordering points to identify the clustering structure. In: SIGMOD ’99: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, Philadelphia, Pennsylvania, United States, pp. 49–60. ACM Press, New York (1999)CrossRefGoogle Scholar
  3. 3.
    Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Eighteenth Annual Conference on Neural Information Processing Systems (2004)Google Scholar
  4. 4.
    Celebi, M.E., Aslandogan, Y.A., Bergstresser, P.R.: Mining biomedical images with density-based clustering. In: ITCC ’05: Proceedings of the International Conference on Information Technology: Coding and Computing, Washington, DC, USA, vol. I, pp. 163–168. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  5. 5.
    Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery 2(2), 169–194 (1998)CrossRefGoogle Scholar
  6. 6.
    Perona, P., Freeman, W.T.: A factorization approach to grouping. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 655–670. Springer, Heidelberg (1998)Google Scholar
  7. 7.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD Record 25(2), 103–114 (1996)CrossRefGoogle Scholar
  8. 8.
    Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: KDD, pp. 58–65 (1998)Google Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2000)Google Scholar
  10. 10.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD ’98: Proceedings ACM SIGMOD International Conference on Management of Data, Seattle, Washington, USA, June 2-4, 1998, pp. 94–105. ACM Press, New York (1998)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Ergun Biçici
    • 1
  • Deniz Yuret
    • 1
  1. 1.Koç University, Rumelifeneri Yolu 34450, Sariyer IstanbulTurkey

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