Document Representation Based on Maximal Frequent Sequence Sets
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.
KeywordsDocument Collection Money Market Vector Space Model Cluster Quality Document Cluster
- 2.Yoelle, S., Fagin, Ronald, Ben-Shaul, Israel Z. y Pelleg, Dan. Ephemeral Document Clustering for Web Applications. IBM Research. Report RJ 10186 (2000)Google Scholar
- 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
- 5.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
- 6.Daucet, A.: Advanced Document Description, a Sequential Approach. Thesis PhD. University of Helsinki Finland (2005) Google Scholar
- 8.Steinbach, M., Karypis, G., Kumar, V.: A Comparison of Document Clustering Techniques. In: Proc. Text mining workshop, KDD (2000)Google Scholar