Automatic Clustering Based on Cluster Nearest Neighbor Distance (CNND) Algorithm

  • Arghya Sur
  • Aritra Chowdhury
  • Jaydeep Ghosh Chowdhury
  • Swagatam Das
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


This article describes a simple and fast algorithm that can automatically detect any number of well separated clusters, which may be of any shape e.g. convex and/or non-convex. This is in contrast to most of the existing clustering algorithms that assume a value for the number of clusters and/or a particular cluster structure. This algorithm is based on the principle that there is a definite threshold in the intra-cluster distances between nearest neighbors in the same cluster. Promising results on both real and artificial datasets have been included to show the effectiveness of the proposed technique.


Cluster nearest Neighbour Clustering Automatic Clustering Various shaped clusters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)MATHGoogle Scholar
  2. 2.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  3. 3.
    Xiong, H., Wu, J.J., Chen, J.: K-means clustering versus validation measures: A data-distribution perspective. IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics 39(2), 318–331 (2009)CrossRefGoogle Scholar
  4. 4.
    Saha, S., Bandyopadhyay, S.: A symmetry based multiobjective clustering technique for automatic evolution of clusters. Pattern Recognition 43, 738–751 (2010)CrossRefMATHGoogle Scholar
  5. 5.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), Google Scholar
  6. 6.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Lett. (2009), doi:10.1016/j.patrec.2009.09.011Google Scholar
  7. 7.
    Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence PAMI-8, 248–255 (1986)CrossRefGoogle Scholar
  8. 8.
    Ben-Hur, A., Guyon, I.: Detecting Stable Clusters Using Principal Component Analysis in Methods of Molecular Biology. Humana Press (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arghya Sur
    • 1
  • Aritra Chowdhury
    • 1
  • Jaydeep Ghosh Chowdhury
    • 1
  • Swagatam Das
    • 2
  1. 1.Dept. of Electronics and Telecomunication EnggJadavpur UniversityKolkataIndia
  2. 2.Electronics and Computer Sciences UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations