Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/14967
metadata.ojs.dc.title: Evaluation and Comparison of Concept Based and N-Grams Based Text Clustering Using SOM
metadata.ojs.dc.creator: Amine, Abdelmalek
Elberrichi, Zakaria
Simonet, Michel
Malki, Mimoun
metadata.ojs.dc.subject: Text clustering
Self-Organizing Maps of Kohonen
N-grams
Concept
Similarity
Reuters21578
metadata.ojs.dc.publisher: Universidade Federal de Lavras
metadata.ojs.dc.date: 1-Mar-2008
metadata.ojs.dc.identifier.citation: AMINE, A.; ELBERRICHI, Z.; SIMONET, M.; MALKI, M. Evaluation and Comparison of Concept Based and N-Grams Based Text Clustering Using SOM. INFOCOMP Journal of Computer Science, Lavras, v. 7, n. 1, p. 27-35, Mar. 2008.
metadata.ojs.dc.identifier: http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/203
metadata.ojs.dc.description: With the great and rapidly growing number of documents available in digital form (Internet, library, CD-Rom…), the automatic classification of texts has become a significant research field and a fundamental task in document processing. This paper deals with unsupervised classification of textual documents also called text clustering using Self-Organizing Maps of Kohonen in two new situations: a conceptual representation of texts and a representation based on n-grams, instead of a representation based on words. The effects of these combinations are examined in several experiments using 4 measurements of similarity. The Reuters-21578 corpus is used for evaluation. The evaluation was done by using the F-measure and the entropy.
metadata.ojs.dc.language: eng
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