Discrete-Time Hopfield Neural Network Based Text Clustering Algorithm

  • Zekeriya Uykan
  • Murat Can Ganiz
  • Çağla Şahinli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7663)


In this study we propose a discrete-time Hopfield Neural Network based clustering algorithm for text clustering for cases L = 2 q where L is the number of clusters and q is a positive integer. The optimum general solution for even 2-cluster case is not known. The main contribution of this paper is as follows: We show that i) sum of intra-cluster distances which is to be minimized by a text clustering algorithm is equal to the Lyapunov (energy) function of the Hopfield Network whose weight matrix is equal to the Laplacian matrix obtained from the document-by-document distance matrix for 2-cluster case; and ii) the Hopfield Network can be iteratively applied to text clustering for L = 2k. Results of our experiments on several benchmark text datasets show the effectiveness of the proposed algorithm as compared to the k-means.


Text clustering discrete-time Hopfield Neural Networks Lyapunov function max-cut graph partitioning 


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  1. 1.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Luxburg, U.V.: A Tutorial on Spectral Clustering. Technical Report TR-149. Max-Planck Institute for Biological Cybernetics (August 2006)Google Scholar
  3. 3.
    Kim, H., Lee, S.: An intelligent information system for organizing online text documents. Knowledge and Information Systems 6(2), 125–149 (2004)Google Scholar
  4. 4.
    Hinneburg, A., Keim, D.: A general approach to clustering in large databases with noise. Knowledge and Information Systems 5(4), 387–415 (2003)CrossRefGoogle Scholar
  5. 5.
    Zhong, S., Ghosh, J.: Generative model-based document clustering: a comparative study. Knowledge and Information Systems 8, 374–384 (2005)CrossRefGoogle Scholar
  6. 6.
    Zanasi, A.: Text Mining and its Applications to Intelligence. Crm and Knowledge Management (Advances in Management Information). WIT Press (2005)Google Scholar
  7. 7.
    Huang, A.: Similarity Measures for Text Document Clustering. In: NZCSRSC 2008, New Zealand (2008)Google Scholar
  8. 8.
    Ding, C.H.Q.: Data clustering: Principal components, Hopfield and self-aggregation networks. NERSC Division, Lawrence Berkeley National Lab., Univ. of California, BerkeleyGoogle Scholar
  9. 9.
    Ding, C.H.Q.: Document retrieval and clustering: from principal component analysis to self-aggregation networks. Lawrence Berkeley National Laboratory, Berkeley, CA 94720Google Scholar
  10. 10.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers (2006)Google Scholar
  11. 11.
    Uykan, Z.: Spectral Based Solutions for (Near) Optimum Channel/Frequency Allocation. In: Proc. of IWSSIP 2011, Sarajevo, BiH (2011)Google Scholar
  12. 12.
    Luxburg, U.V., Belkin, M., Bousquet, O.: Consistency of spectral clustering. Annals of Statistics 36, 555–586 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Forman, G., Cohen, I.: Learning from Little: Comparison of Classifiers Given Little Training. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 161–172. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11, 10–18 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zekeriya Uykan
    • 1
  • Murat Can Ganiz
    • 2
  • Çağla Şahinli
    • 2
  1. 1.Electronics and Communications Engineering Dept.Dogus UniversityIstanbulTurkey
  2. 2.Computer Engineering Dept.Dogus UniversityIstanbulTurkey

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