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Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/14967
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Campo DCValorIdioma
dc.creatorAmine, Abdelmalek-
dc.creatorElberrichi, Zakaria-
dc.creatorSimonet, Michel-
dc.creatorMalki, Mimoun-
dc.date2008-03-01-
dc.date.accessioned2017-08-01T21:08:39Z-
dc.date.available2017-08-01T21:08:39Z-
dc.date.issued2017-08-01-
dc.identifierhttp://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/203-
dc.identifier.citationAMINE, 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.-
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/14967-
dc.descriptionWith 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.-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherUniversidade Federal de Lavras-
dc.relationhttp://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/203/188-
dc.rightsCopyright (c) 2016 INFOCOMP Journal of Computer Science-
dc.sourceINFOCOMP; Vol 7 No 1 (2008): March, 2008; 27-35-
dc.source1982-3363-
dc.source1807-4545-
dc.subjectText clustering-
dc.subjectSelf-Organizing Maps of Kohonen-
dc.subjectN-grams-
dc.subjectConcept-
dc.subjectSimilarity-
dc.subjectReuters21578-
dc.titleEvaluation and Comparison of Concept Based and N-Grams Based Text Clustering Using SOM-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
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