Evaluation and Comparison of Concept Based and N-Grams Based Text Clustering Using SOM - Amine, Abdelmalek; Elberrichi, Zakaria; Simonet, Michel; Malki, Mimoun (INFOCOMP Journal of Computer Science, 2008)
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.