Abstract
In document clustering, documents are commonly represented through the vector space model as a word vector where the features correspond to the words of the documents. However, there are a lot of words in a document set; therefore the vector size could be enormous. Also, the vector space model does not take into account the word order that could be useful to group similar documents. In order to reduce these disadvantages, we propose a new document representation in which each document is represented as a set of its maximal frequent sequences. The proposed document representation is applied for document clustering and the quality of the clustering is evaluated through internal and external measures, the results are compared with those obtained with the vector space model.
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Hernández-Reyes, E., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., García-Hernández, R.A. (2006). Document Representation Based on Maximal Frequent Sequence Sets. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_88
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DOI: https://doi.org/10.1007/11892755_88
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