Improving Alternative Text Clustering Quality in the Avoiding Bias Task with Spectral and Flat Partition Algorithms

  • M. Eduardo Ares
  • Javier Parapar
  • Álvaro Barreiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6262)

Abstract

The problems of finding alternative clusterings and avoiding bias have gained popularity over the last years. In this paper we put the focus on the quality of these alternative clusterings, proposing two approaches based in the use of negative constraints in conjunction with spectral clustering techniques. The first approach tries to introduce these constraints in the core of the constrained normalised cut clustering, while the second one combines spectral clustering and soft constrained k-means. The experiments performed in textual collections showed that the first method does not yield good results, whereas the second one attains large increments on the quality of the results of the clustering while keeping low similarity with the avoided grouping.

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References

  1. 1.
    Gondek, D., Hofmann, T.: Non-redundant data clustering. In: ICDM 2004: Proceedings of the Fourth IEEE International Conference on Data Mining, pp. 75–82. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  2. 2.
    Davidson, I., Qi, Z.: Finding alternative clustering using constraints. In: ICDM 2008: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  3. 3.
    Ares, M.E., Parapar, J., Barreiro, A.: Avoiding bias in text clustering using constrained k-means and may-not-links. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 322–329. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Ji, X., Xu, W., Zhu, S.: Document clustering with prior knowledge. In: SIGIR 2006: Proceedings of the 29th Annual international ACM SIGIR conference on Research and development in information retrieval, pp. 405–412. ACM, New York (2006)CrossRefGoogle Scholar
  5. 5.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  6. 6.
    Ding, C.: A tutorial on spectral clustering. In: Tutorial presented at ICML 2004: 21st International Conference on Machine Learning (2004)Google Scholar
  7. 7.
    von Luxburg, U.: A tutorial on spectral clustering. Technical Report TR-149, Max Planck Institute for Biological Cybernetics (2006)Google Scholar
  8. 8.
    McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  9. 9.
    Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: ICML 2001: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577–584, Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  10. 10.
    Pantel, P., Lin, D.: Document clustering with committees. In: SIGIR 2002: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 199–206. ACM Press, New York (2002)CrossRefGoogle Scholar
  11. 11.
    Rosell, M., Kann, V., Litton, J.E.: Comparing comparisons: Document clustering evaluation using two manual classifications. In: Proceedings of the International Conference on Natural Language Processing (2004)Google Scholar
  12. 12.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)MATHGoogle Scholar
  13. 13.
    Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC, Boca Raton (2008)Google Scholar
  14. 14.
    Bae, E., Bailey, J.: COALA: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In: ICDM 2006: Proceedings of the Sixth International Conference on Data Mining, pp. 53–62. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  15. 15.
    Davidson, I., Qi, Z.: Finding alternative clustering using constraints. In: ICDM 2008: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  16. 16.
    Cohn, D., Caruana, R., McCallum, A.: Semi-supervised clustering with user feedback. Technical Report TR-2003-1892, Cornell University (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • M. Eduardo Ares
    • 1
  • Javier Parapar
    • 1
  • Álvaro Barreiro
    • 1
  1. 1.IRLab, Department of Computer ScienceUniversity of A CoruñaSpain

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