SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
MAIL: mining sequential patterns with wildcards
International Journal of Data Mining and Bioinformatics
Dynamic context for document search and recovery
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Coupled attribute analysis on numerical data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Despite the advantages of the traditional vector space model (VSM) representation, there are known deficiencies concerning the term independence assumption. The high dimensionality and sparsity of the text feature space and phenomena such as polysemy and synonymy can only be handled if a way is provided to measure term similarity. Many approaches have been proposed that map document vectors onto a new feature space where learning algorithms can achieve better solutions. This paper presents the global term context vector-VSM (GTCV-VSM) method for text document representation. It is an extension to VSM that: (i) it captures local contextual information for each term occurrence in the term sequences of documents; (ii) the local contexts for the occurrences of a term are combined to define the global context of that term; (iii) using the global context of all terms a proper semantic matrix is constructed; (iv) this matrix is further used to linearly map traditional VSM (Bag of Words—BOW) document vectors onto a ‘semantically smoothed’ feature space where problems such as text document clustering can be solved more efficiently. We present an experimental study demonstrating the improvement of clustering results when the proposed GTCV-VSM representation is used compared with traditional VSM-based approaches.