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.
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Hernández-Reyes, E., García-Hernández, R.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2006). Document Clustering Based on Maximal Frequent Sequences. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds) Advances in Natural Language Processing. FinTAL 2006. Lecture Notes in Computer Science(), vol 4139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816508_27
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DOI: https://doi.org/10.1007/11816508_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37334-6
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