Word Clustering with Validity Indices

  • Ahmad El Sayed
  • Julien Velcin
  • Djamel Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)


The goal of any clustering algorithm producing flat partitions of data is to find the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in the clustering process, which can lead to an objective selection of the optimal number of clusters. In this paper, we provide two main contributions. Firstly, since validity indices have been mostly studied in small dimensional datasets, we have chosen to evaluate them in a real-world task: agglomerative clustering of words. Secondly, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.


Cluster Process Validity Index Relative Index Optimal Partition Agglomerative Cluster 
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.
    Cimiano, P., Hotho, A., Staab, S.: Comparing conceptual, divise and agglomerative clustering for learning taxonomies from text. In: ECAI, pp. 435–439 (2004)Google Scholar
  2. 2.
    Qiu, Y., Frei, H.-P.: Concept based query expansion. In: SIGIR 1993: Proc. of the 16th annual Int. ACM SIGIR Conf. on Research and development in information retrieval, pp. 160–169. ACM Press, New York (1993)CrossRefGoogle Scholar
  3. 3.
    Stokoe, C., Oakes, M.P., Tait, J.: Word sense disambiguation in information retrieval revisited. In: SIGIR, pp. 159–166 (2003)Google Scholar
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)Google Scholar
  5. 5.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)zbMATHGoogle Scholar
  6. 6.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Clustering validity checking methods: Part ii. SIGMOD Record 31(3), 19–27 (2002)CrossRefGoogle Scholar
  7. 7.
    Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50(2), 159–179 (1985), CrossRefGoogle Scholar
  8. 8.
    Dunn, J.C.: Well separated clusters and optimal fuzzy paritions. Journal Cybern. 4, 95–104 (1974)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2) (1979)Google Scholar
  10. 10.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a dataset via the gap statistic. Dept. of Statistics, Stanford University., Tech. Rep. (2000)Google Scholar
  11. 11.
    Raskutti, B., Leckie, C.: An evaluation of criteria for measuring the quality of clusters. In: IJCAI, pp. 905–910 (1999)Google Scholar
  12. 12.
    Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Pacific Symposium on Biocomputing, pp. 6–17 (2002)Google Scholar
  13. 13.
    Bezdek, J.C., Li, W., Attikiouzel, Y., Windham, M.P.: A geometric approach to cluster validity for normal mixtures. Soft Comput. 1(4), 166–179 (1997)Google Scholar
  14. 14.
    Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55(3), 311–331 (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    Fraley, C., Raftery, A.E.: How many clusters? which clustering method? answers via model-based cluster analysis. Comput. J. 41(8), 578–588 (1998)zbMATHCrossRefGoogle Scholar
  16. 16.
    Rissanen, J.: Stochastic complexity in statistical inquiry. World Scientific Publishing Co., Singapore (1989)zbMATHGoogle Scholar
  17. 17.
    Pelleg, D., Moore, A.W.: X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)Google Scholar
  18. 18.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD Conf., pp. 94–105 (1998)Google Scholar
  19. 19.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  20. 20.
    Pedersen, T., Kulkarni, A.: Selecting the ”right” number of senses based on clustering criterion functions. In: EACL (2006)Google Scholar
  21. 21.
    Landes, S., Leacock, C., Tengi, R.I.: Building semantic concordances. M. Press, pp. 199–216 (1998)Google Scholar
  22. 22.
    Harris, Z.S.: Distributional structure. Oxford University Press, Oxford (1985)Google Scholar
  23. 23.
    Turney, P.D.: Mining the web for synonyms: Pmi-ir versus lsa on toefl. In: EMCL 2001: Proc. of the 12th European Conf. on Machine Learning, pp. 491–502. Springer, London (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ahmad El Sayed
    • 1
  • Julien Velcin
    • 1
  • Djamel Zighed
    • 1
  1. 1.ERIC LaboratoryUniversity of Lyon 2 

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