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)


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.


Document Collection Money Market Vector Space Model Term Weighting Cluster Quality 
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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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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