Multi Density DBSCAN

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)


Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases.DBSCAN clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. DBSCAN cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities and without the need to input the value of Eps because our algorithm can find the appropriate value for each cluster individually by replacing Eps by Local cluster density.


Clustering Arbitrary Shape DBSCAN variable Densities 


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  1. 1.
    Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications, pp. 6–7. SIAM, Philadelphia (2007)CrossRefzbMATHGoogle Scholar
  2. 2.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density based algorithm for discovering clusters in large spatial data sets with noise. In: 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)Google Scholar
  3. 3.
    Hinneburg, A., Keim, D.: An efficient approach to clustering in large multimedia data sets with noise. In: 4th International Conference on Knowledge Discovery and Data Mining, pp. 58–65 (1998)Google Scholar
  4. 4.
    Fahim, A.M., Saake, G., Salem, A.M., Torkey, F.A., Ramadan, M.A.: Improved DBSCAN for Spatial Databases with Noise and Different Densities. Georgian Electronic Scientific Journal: Computer Science and Telecommunications, 53–60 (2009)Google Scholar
  5. 5.
    Borah, B., Bhattacharyya, D.K.: DDSC: A Density Differentiated Spatial Clustering Technique. Journal of Computers 3(2), 72–79 (2008)CrossRefGoogle Scholar
  6. 6.
    Estivill-Castro, V., Lee, I.: AUTOCLUST: Automatic Clustering via Boundary Extraction for Mining Massive Point-Dta Sets. In: Abrahart, J., Carlisle, B.H. (eds.) Proc. Of the 5th Int. Conf. on Geocomputation (2000)Google Scholar
  7. 7.
    Openshaw, S.: A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets. International Journal of GIS 1(4), 335–358 (1987)Google Scholar
  8. 8.
    Ng, R.T., Han, J.: Efficient and Effective Clustering Method for Spatial Data Mining. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), pp. 144–155 (1994)Google Scholar
  9. 9.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 103–114 (1996)Google Scholar
  10. 10.
    Karypis, G., Han, E., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. IEEE Computer: Special Issue on Data Analysis and Mining 32(8), 68–75 (1999)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Islamic University of GazaGazaPalestine

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