A Graph-Based Clustering Method and Its Applications

  • Pasquale Foggia
  • Gennaro Percannella
  • Carlo Sansone
  • Mario Vento
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)


In this paper we present a graph-based clustering method particularly suited for dealing with data that do not come from a Gaussian or a spherical distribution. It can be used for detecting clusters of any size and shape, without the need of specifying neither the actual number of clusters nor other parameters.

The method has been tested on data coming from two different computer vision applications. A comparison with other three state-of-the-art algorithms was also provided, demonstrating the effectiveness of the proposed approach.


Cluster Algorithm Minimum Span Tree News Video Cluster Detection Mammographic Image 
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.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA (1988)zbMATHGoogle Scholar
  3. 3.
    Kohonen, T.: Self-organizing maps. Springer, Heidelberg (1995)Google Scholar
  4. 4.
    Juszczak, P.: Learning to recognise. A study on one-class classification and active learning, PhD thesis, Delft University of Technology (2006), ISBN: 978-90-9020684-4Google Scholar
  5. 5.
    Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, 2967–2991 (2003)zbMATHCrossRefGoogle Scholar
  6. 6.
    Wu, Z., Leahy, R.: An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation. IEEE Trans. on PAMI 15(11), 1101–1113 (1993)Google Scholar
  7. 7.
    Zahn, C.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers C-20, 68–86 (1971)CrossRefGoogle Scholar
  8. 8.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)zbMATHGoogle Scholar
  9. 9.
    van Dongen, S.M.: Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht (2000)Google Scholar
  10. 10.
    Kannan, R., Vampala, S., Vetta, A.: On Clustering: Good, Bad and Spectral. In: Foundations of Computer Science 2000, pp. 367–378 (2000)Google Scholar
  11. 11.
    Gaertler, M.: Clustering with spectral methods. Master’s thesis, Universitat Konstanz (2002)Google Scholar
  12. 12.
    De Santo, M., Percannella, G., Sansone, C., Vento, M.: Combining experts for anchorperson shot detection in news videos. Pattern Analysis and Applications 7(4), 447–460 (2005)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Horowitz, E., Sahni, S.: Fundamentals of Computer Algorithms. Computer Science Press (1978)Google Scholar
  14. 14.
    Brandes, U., Gaertler, M., Wagner, D.: Experiments on Graph Clustering Algorithms. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 568–579. Springer, Heidelberg (2003)Google Scholar
  15. 15.
    Sajda, P., Spence, C., Pearson, J.: Learning contextual relationships in mammograms using a hierarchical pyramid neural network. IEEE Trans. on Medical Imaging 21(3), 239–250 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pasquale Foggia
    • 1
  • Gennaro Percannella
    • 2
  • Carlo Sansone
    • 1
  • Mario Vento
    • 2
  1. 1.Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II, Via Claudio, 21 I-80125 NapoliItaly
  2. 2.Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università di Salerno, via P.te Don Melillo, I-84084 Fisciano (SA)Italy

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