An Optimized Approach for Density Based Spatial Clustering Application with Noise

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)


The density based algorithms such as DBSCAN is considered as one of the most common and powerful algorithms in data clustering with the noise datasets. DBSCAN based algorithm’s is able to find out clusters with the different shape and variable size. However it is failed to detect the correct clusters, if there is density variation within the clusters. This paper presents new way to solve the problem of detecting the clusters of varying density which most of the DBSCAN based algorithms can’t deal with it correctly. Our proposed approach is depending on oscillation of clusters which is obtained by applying basic DBSCAN algorithm to conflation it in a new clusters, the proposed algorithm help to decide whether the different density regions belong to the same cluster or not. The experimental results showed that the proposed clustering algorithm gives satisfied results on different Data sets.


Core point DBSCAN Eps RegionQuery MinPts 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers (2006)Google Scholar
  2. 2.
    Guojun, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications, SIAM, Philadel-phia, ASA, Alexandria, VA. ASA-SIAM Series on Statistics and Applied Probability (2007)Google Scholar
  3. 3.
    Karypis, G., Han, E.H., Kumar, V.: CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. Computer 32(8), 68–75 (1999)CrossRefGoogle Scholar
  4. 4.
    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, KDD 1996, pp. 226–231. Portland, Oregon (1996)Google Scholar
  5. 5.
    Ankerst, M., Breunig, M., Kriegel, H.P., Sander, J.: OPTICS: Ordering points to identify the clustering structure. In: Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data, SIGMOD 1996 (1999)Google Scholar
  6. 6.
    Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: Proc. 1998 Int. Conf. Knowledge Discovery and Data mining, KDD 1998 (1998)Google Scholar
  7. 7.
    Berkhin, P.: Survey of Clustering Data Mining Techniques, Accrue Software, Technical Report, nnnn (2002)Google Scholar
  8. 8.
    Parimala, M., Lopez, D., Senthilkumar, N.C.: A Survey on Density Based Clustering Algorithms for Mining Large Spatial Databases. International Journal of Advanced Science and Technology 31 (June 2011)Google Scholar
  9. 9.
    Ng, R., Han, J.: Efficient and effective clustering method for spatial data mining, Santiago, Chile, pp. 144–155 (September 1994)Google Scholar
  10. 10.
    Guha, S., Rastogi, R., Shim, K.: CURE: An efficient clustering algorithm for large databases, Seattle, WA, pp. 73–84 (June 1998)Google Scholar
  11. 11.
    Guha, S., Rastogi, R., Shim, K.: ROCK: A robust clustering algorithm for categorical attributes, Sydney, Australia, pp. 512–521 (March 1999)Google Scholar
  12. 12.
    Roy, S., Bhattacharyya, D.K.: An approach to find embedded clusters using density based techniques. In: Chakraborty, G. (ed.) ICDCIT 2005. LNCS, vol. 3816, pp. 523–535. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Borah, B., Bhattacharyya, D.K.: DDSC: A Density Differentiated Spatial Clustering Technique. Journal of Computers 3(2) (February 2008)Google Scholar
  14. 14.
    Borach, B., Bhattacharya, D.K.: A Clustering Technique using Density Difference. In: Proceedings of International Conference on Signal Processing, Communications and Networking, pp. 585–588 (2007)Google Scholar
  15. 15.
    Ram, A., Jalal, S., Jalal, A.S., Kumar, M.: DVBSCAN: A Density based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases. International Journal of Computer Applications (0975 – 8887) 3(6) (June 2010)Google Scholar
  16. 16.
    UCI Machine Learning Repository,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electronics & Information TechnologyNational Informatics Center (NIC)New DelhiIndia
  2. 2.Department of Computer ScienceNational Institute of TechnologyJalandharIndia

Personalised recommendations