Document Clustering Based on Maximal Frequent Sequences

  • Edith Hernández-Reyes
  • Rene A. García-Hernández
  • J. A. Carrasco-Ochoa
  • J. Fco. Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)

Abstract

Document clustering has the goal of discovering groups with similar documents. The success of the document clustering algorithms depends on the model used for representing these documents. Documents are commonly represented with the vector space model based on words or n-grams. However, these representations have some disadvantages such as high dimensionality and loss of the word sequential order. In this work, we propose a new document representation in which the maximal frequent sequences of words are used as features of the vector space model. The proposed model efficiency is evaluated by clustering different document collections and compared against the vector space model based on words and n-grams, through internal and external measures.

References

  1. 1.
    Su, Z., Zhang, L., Pan, Y.: Document Clustering Based on Vector Quatization and Growing-Cell Structure, pp. 326–336. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Yoelle, S., Fagin, Ronald, Ben-Shaul, Pelleg, I.Z.y., Dan: Ephemeral Document Clustering for Web Applications. IBM Research. Report RJ 10186 (2000)Google Scholar
  3. 3.
    Salton, G., Wang, A., Yang, C.S.: A Vector Space Model for Information Retrieval. Journal of the American Society for information Science, 613–620 (1975)Google Scholar
  4. 4.
    Jing, L., Ng, M.K., Xu, J., Huang, J.Z.: Subspace Clustering of Text Documents with Feature Weighting K-Means Algorithm. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 802–812. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Steinbach, M., Karypis, G., Kumar, V.: A Comparison of Document Clustering Techniques. In: Proc. Text mining workshop, KDD (2000)Google Scholar
  6. 6.
    Pantel, P., Lin, D.: Efficiently Clustering Documents with Committees. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS, vol. 2417, pp. 424–433. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Manning, C.D., Schütze, H.: Foundations of Statical Natural Language Processing. Massachussets Institute of Technology (2001)Google Scholar
  9. 9.
    Luo, X., Zincir-Heywood, N.: Analyzing the Temporal Sequences for Text Categorization, pp. 498–505. Springer, Heildeberg (2004)Google Scholar
  10. 10.
    Ahonen-Myka, H.: Finding All Maximal Frequent Sequences in Text. In: Proc. of the ICML 1999 Workshop on Machine Learning in Text Data Analysis, pp. 11–17 (1999)Google Scholar
  11. 11.
    Doucet, A.: Advanced Document Description, a Sequential Approach. Thesis PhD. University of Helsinki Finland (2005)Google Scholar
  12. 12.
    García-Hernández, R.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 478–486. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Edith Hernández-Reyes
    • 1
  • Rene A. García-Hernández
    • 1
  • J. A. Carrasco-Ochoa
    • 1
  • J. Fco. Martínez-Trinidad
    • 1
  1. 1.Optics and ElectronicsNational Institute for AstrophysicsTonantzintla, PueblaMéxico

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