On the Relative Hardness of Clustering Corpora

  • David Pinto
  • Paolo Rosso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4629)


Clustering is often considered the most important unsupervised learning problem and several clustering algorithms have been proposed over the years. Many of these algorithms have been tested on classical clustering corpora such as Reuters and 20 Newsgroups in order to determine their quality. However, up to now the relative hardness of those corpora has not been determined. The relative clustering hardness of a given corpus may be of high interest, since it would help to determine whether the usual corpora used to benchmark the clustering algorithms are hard enough. Moreover, if it is possible to find a set of features involved in the hardness of the clustering task itself, specific clustering techniques may be used instead of general ones in order to improve the quality of the obtained clusters. In this paper, we are presenting a study of the specific feature of the vocabulary overlapping among documents of a given corpus. Our preliminary experiments were carried out on three different corpora: the train and test version of the R8 subset of the Reuters collection and a reduced version of the 20 Newsgroups (Mini20Newsgroups). We figured out that a possible relation between the vocabulary overlapping and the F-Measure may be introduced.


Cluster Algorithm Unlabeled Data Test Version Text Corpus Related Category 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Debole, F., Sebastiani, F.: An analysis of the relative hardness of reuters-21578 subsets. Journal of the American Society for Information Science and Technology 56(6), 584–596 (2005)CrossRefGoogle Scholar
  2. 2.
    Zaïane, O.R.: Principles of knowledge discovery in databases - ch. 8: Data clustering (1999), online-textbook
  3. 3.
    Meyer zu Eissen, S., Stein, B.: Analysis of clustering algorithms for web-based search. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2002. LNCS (LNAI), vol. 2569, pp. 168–178. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Meyer zu Eissen, S.: On Information Need and Categorizing Search. Dissertation, University of Paderborn (2007)Google Scholar
  5. 5.
    Bezdek, J.C., Pal, N.R.: Cluster validation with generalized dunn’s indices. In: 2nd International two-stream conference on ANNES, pp. 190–193 (1995)Google Scholar
  6. 6.
    Bezdek, J.C., Li, W.Q., Attikiouzel, Y., Windham, M.: Geometric approach to cluster validity for normal mixtures. Soft Computing 1(4), 166–179 (1997)Google Scholar
  7. 7.
    Stein, B., Nigemman, O.: On the nature of structure and its identification. In: Widmayer, P., Neyer, G., Eidenbenz, S. (eds.) WG 1999. LNCS, vol. 1665, pp. 122–134. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Kang, N.O., Gelbukh, A., Han, S.Y.: Ppchecker: Plagiarism pattern checker in document copy detection. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2006. LNCS (LNAI), vol. 4188, pp. 661–667. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Pinto, D., Jiménez-Salazar, H., Rosso, P.: Clustering abstracts of scientific texts using the transition point technique. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 536–546. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • David Pinto
    • 1
    • 2
  • Paolo Rosso
    • 1
  1. 1.Department of Information Systems and Computation, Polytechnic University of Valencia, Spain, Faculty of Computer Science 
  2. 2.B. Autonomous University of PueblaMexico

Personalised recommendations